diff --git a/optimas/generators/xopt/__init__.py b/optimas/generators/xopt/__init__.py
new file mode 100644
index 00000000..611f86b0
--- /dev/null
+++ b/optimas/generators/xopt/__init__.py
@@ -0,0 +1 @@
+from .nelder_mead import NelderMeadGenerator
diff --git a/optimas/generators/xopt/nelder_mead.py b/optimas/generators/xopt/nelder_mead.py
new file mode 100644
index 00000000..48d935ee
--- /dev/null
+++ b/optimas/generators/xopt/nelder_mead.py
@@ -0,0 +1,74 @@
+from typing import List, Optional
+
+import numpy as np
+import pandas as pd
+
+from xopt import VOCS
+from xopt.generators.scipy.neldermead import (
+    NelderMeadGenerator as XoptNelderMeadGenerator,
+)
+
+from optimas.core import (
+    Objective,
+    VaryingParameter,
+    Parameter,
+    Evaluation,
+    Trial,
+)
+from optimas.generators.base import Generator
+
+
+class NelderMeadGenerator(Generator):
+    def __init__(
+        self,
+        varying_parameters: List[VaryingParameter],
+        objectives: List[Objective],
+        analyzed_parameters: Optional[List[Parameter]] = None,
+        save_model: Optional[bool] = False,
+        model_save_period: Optional[int] = 5,
+        model_history_dir: Optional[str] = "model_history",
+    ) -> None:
+        super().__init__(
+            varying_parameters=varying_parameters,
+            objectives=objectives,
+            analyzed_parameters=analyzed_parameters,
+            save_model=save_model,
+            model_save_period=model_save_period,
+            model_history_dir=model_history_dir,
+        )
+        self._create_xopt_generator()
+
+    def _ask(self, trials: List[Trial]) -> List[Trial]:
+        n_trials = len(trials)
+        xopt_trials = self.xopt_gen.generate(n_trials)
+        if xopt_trials:
+            for trial, xopt_trial in zip(trials, xopt_trials):
+                trial.parameter_values = [
+                    xopt_trial[var.name] for var in self.varying_parameters
+                ]
+        return trials
+
+    def _tell(self, trials: List[Trial]) -> None:
+        # pd.DataFrame({"x1": [0.5], "x2": [5.0], "y1": [0.5], "c1": [0.5]})
+        for trial in trials:
+            data = {}
+            for oe in trial.objective_evaluations:
+                data[oe.parameter.name] = [oe.value]
+            df = pd.DataFrame(data)
+            self.xopt_gen.add_data(df)
+
+    def _create_xopt_generator(self):
+        variables = {}
+        for var in self.varying_parameters:
+            variables[var.name] = [var.lower_bound, var.upper_bound]
+        objectives = {}
+        for objective in self.objectives:
+            name = objective.name
+            objectives[name] = "MINIMIZE" if objective.minimize else "MAXIMIZE"
+        vocs = VOCS(variables=variables, objectives=objectives)
+        initial_point = {}
+        for var in self.varying_parameters:
+            initial_point[var.name] = (var.lower_bound + var.upper_bound) / 2
+        self.xopt_gen = XoptNelderMeadGenerator(
+            vocs=vocs, initial_point=initial_point
+        )
diff --git a/tests/test_neldermead.py b/tests/test_neldermead.py
new file mode 100644
index 00000000..dcccb35e
--- /dev/null
+++ b/tests/test_neldermead.py
@@ -0,0 +1,68 @@
+import numpy as np
+
+from optimas.explorations import Exploration
+from optimas.generators.xopt import NelderMeadGenerator
+from optimas.evaluators import FunctionEvaluator
+from optimas.core import VaryingParameter, Objective
+
+
+def eval_func(input_params, output_params):
+    """Evaluation function for single-fidelity test"""
+    x0 = input_params["x0"]
+    x1 = input_params["x1"]
+    result = -(x0 + 10 * np.cos(x0)) * (x1 + 5 * np.cos(x1))
+    output_params["f"] = result
+
+
+def test_neldermead():
+    """Test that grid sampling generates the expected configurations."""
+
+    # Create varying parameters.
+    names = ["x0", "x1"]
+    lower_bounds = [-3.0, 2.0]
+    upper_bounds = [1.0, 5.0]
+    vars = []
+    n_steps = [7, 15]
+    for name, lb, ub in zip(names, lower_bounds, upper_bounds):
+        vars.append(VaryingParameter(name, lb, ub))
+
+    # Set number of evaluations.
+    n_evals = np.prod(n_steps)
+
+    # Define objective.
+    obj = Objective("f", minimize=False)
+
+    # Create generator and run exploration.
+    gen = NelderMeadGenerator(varying_parameters=vars, objectives=[obj])
+    ev = FunctionEvaluator(function=eval_func)
+    exploration = Exploration(
+        generator=gen,
+        evaluator=ev,
+        max_evals=n_evals,
+        sim_workers=1,
+        exploration_dir_path="./tests_output/test_neldermead",
+    )
+    exploration.run()
+
+    # Get generated points.
+    h = exploration.history
+    h = h[h["sim_ended"]]
+    x0_gen = h["x0"]
+    x1_gen = h["x1"]
+
+    # Get expected 1D steps along each variable.
+    x0_steps = np.linspace(lower_bounds[0], upper_bounds[0], n_steps[0])
+    x1_steps = np.linspace(lower_bounds[1], upper_bounds[1], n_steps[1])
+
+    # Check that the scan along each variable is as expected.
+    np.testing.assert_array_equal(np.unique(x0_gen), x0_steps)
+    np.testing.assert_array_equal(np.unique(x1_gen), x1_steps)
+
+    # Check that for every x0 step, the expected x1 steps are performed.
+    for x0_step in x0_steps:
+        x1_in_x0_step = x1_gen[x0_gen == x0_step]
+        np.testing.assert_array_equal(x1_in_x0_step, x1_steps)
+
+
+if __name__ == "__main__":
+    test_neldermead()