diff --git a/greedy_methods/job_sequence.py b/greedy_methods/job_sequence.py
new file mode 100644
index 000000000000..d28fd883e3a0
--- /dev/null
+++ b/greedy_methods/job_sequence.py
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+"""
+Given a list of tasks, each with a deadline and reward,
+The maximum number of tasks that can be scheduled to maximize reward.
+We can only complete one task at a time, and each task takes 1 unit 
+of time to complete. Once a task has passed its deadline, it can no 
+longer be scheduled.
+
+Example : 
+tasks_info = [(4, 20), (1, 10), (1, 40), (1, 30)]
+max_tasks will return (2, [2, 0]) - 
+which is by scheduling the tasks with rewards 40, 20 
+
+This problem can be solved using the concept of "GREEDY ALGORITHM".
+Time Complexity - O(n log n)
+
+We iterate over the tasks array once, sorting it in descending order of 
+reward. Then we iterate over the sorted tasks array once more, scheduling 
+each task if its deadline is greater than the current time.The greedy choice 
+at each point is to either schedule the current task if its deadline is 
+greater than the current time, or skip it otherwise.
+"""
+
+class Task:
+    def __init__(self, id, deadline, reward):
+        self.id = id
+        self.deadline = deadline
+        self.reward = reward
+
+def max_tasks(tasks_info : list[tuple[int]]) -> int:
+    """
+    >>> max_tasks([(4, 20), (1, 10), (1, 40), (1, 30)])
+    (2, [2, 0])
+    >>> max_tasks([(1, 10), (2, 20), (3, 30), (2, 40)])
+    (2, [3, 2])
+    """
+    tasks = [Task(i, d, p) for i, (d, p) in enumerate(tasks_info)]
+
+    tasks.sort(key=lambda task: task.reward, reverse=True)
+
+    schedule = []
+    current_time = 0
+
+    for task in tasks:
+        if task.deadline > current_time:
+            schedule.append(task.id)
+            current_time += 1
+
+    return len(schedule),schedule
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
+    
+    print(max_tasks([(4, 20), (1, 10), (1, 40), (1, 30)]))