diff --git a/botorch/acquisition/input_constructors.py b/botorch/acquisition/input_constructors.py
index b1f0b39acd..dba1d55f29 100644
--- a/botorch/acquisition/input_constructors.py
+++ b/botorch/acquisition/input_constructors.py
@@ -1532,6 +1532,7 @@ def optimize_objective(
     model: Model,
     bounds: Tensor,
     q: int,
+    acq_function: Optional[AcquisitionFunction] = None,
     objective: Optional[MCAcquisitionObjective] = None,
     posterior_transform: Optional[PosteriorTransform] = None,
     linear_constraints: Optional[tuple[Tensor, Tensor]] = None,
@@ -1574,18 +1575,21 @@ def optimize_objective(
     if optimizer_options is None:
         optimizer_options = {}
 
-    if objective is not None:
-        sampler_cls = SobolQMCNormalSampler if qmc else IIDNormalSampler
-        acq_function = qSimpleRegret(
-            model=model,
-            objective=objective,
-            posterior_transform=posterior_transform,
-            sampler=sampler_cls(sample_shape=torch.Size([mc_samples]), seed=seed_inner),
-        )
-    else:
-        acq_function = PosteriorMean(
-            model=model, posterior_transform=posterior_transform
-        )
+    if acq_function is None:
+        if objective is None:
+            acq_function = PosteriorMean(
+                model=model, posterior_transform=posterior_transform
+            )
+        else:
+            sampler_cls = SobolQMCNormalSampler if qmc else IIDNormalSampler
+            acq_function = qSimpleRegret(
+                model=model,
+                objective=objective,
+                posterior_transform=posterior_transform,
+                sampler=sampler_cls(
+                    sample_shape=torch.Size([mc_samples]), seed=seed_inner
+                ),
+            )
 
     if fixed_features:
         acq_function = FixedFeatureAcquisitionFunction(
diff --git a/test/acquisition/test_input_constructors.py b/test/acquisition/test_input_constructors.py
index c8a3aa530b..a8e876eaf3 100644
--- a/test/acquisition/test_input_constructors.py
+++ b/test/acquisition/test_input_constructors.py
@@ -220,6 +220,16 @@ def test_optimize_objective(self, mock_optimize_acqf):
         mock_model = self.mock_model
         bounds = torch.rand(2, len(self.bounds))
 
+        with self.subTest("scalarObjective_acqusitionFunction"):
+            optimize_objective(
+                model=mock_model,
+                bounds=bounds,
+                q=1,
+                acq_function=UpperConfidenceBound(model=mock_model, beta=0.1),
+            )
+            kwargs = mock_optimize_acqf.call_args[1]
+            self.assertIsInstance(kwargs["acq_function"], UpperConfidenceBound)
+
         A = torch.rand(1, bounds.shape[-1])
         b = torch.zeros([1, 1])
         idx = A[0].nonzero(as_tuple=False).squeeze()