diff --git a/evoagentx/optimizers/evoprompt_optimizer.py b/evoagentx/optimizers/evoprompt_optimizer.py
index caad88c8..44562a0e 100644
--- a/evoagentx/optimizers/evoprompt_optimizer.py
+++ b/evoagentx/optimizers/evoprompt_optimizer.py
@@ -16,6 +16,7 @@
 
 import asyncio
 import json
+import math
 import random
 import re
 import os
@@ -479,7 +480,8 @@ async def _evaluate_combination_list(self, combinations: List[Dict], benchmark:
     def _generate_combinations(self, node_populations: Dict[str, List[str]]) -> List[Dict[str, str]]:
         node_names = list(node_populations.keys())
         node_prompts = [node_populations[node] for node in node_names]
-        total_possible = np.prod([len(p) for p in node_prompts if p]) if all(p for p in node_prompts) else 0
+        # Search spaces can exceed the range of NumPy's fixed-width integers.
+        total_possible = math.prod(len(p) for p in node_prompts)
 
         if total_possible == 0:
             logger.warning("Cannot generate combinations, one or more node populations are empty.")
diff --git a/tests/src/optimizers/test_evoprompt_combinations.py b/tests/src/optimizers/test_evoprompt_combinations.py
new file mode 100644
index 00000000..46af3fdd
--- /dev/null
+++ b/tests/src/optimizers/test_evoprompt_combinations.py
@@ -0,0 +1,57 @@
+import itertools
+import random
+
+import pytest
+
+from evoagentx.optimizers.evoprompt_optimizer import GAOptimizer
+
+
+@pytest.fixture
+def optimizer():
+    """Create a sampler without initializing the LLM-backed evolution agents."""
+    optimizer = GAOptimizer.__new__(GAOptimizer)
+    optimizer.population_size = 5
+    optimizer.combination_sample_size = None
+    return optimizer
+
+
+@pytest.mark.parametrize("node_count,prompts_per_node", [(16, 16), (19, 10)])
+@pytest.mark.parametrize("sample_size", [None, 7])
+def test_sample_large_combination_space(optimizer, monkeypatch, node_count, prompts_per_node, sample_size):
+    """Large search spaces still yield the requested number of valid candidates."""
+    optimizer.combination_sample_size = sample_size
+    populations = {
+        f"node_{i}": [f"prompt_{j}" for j in range(prompts_per_node)]
+        for i in range(node_count)
+    }
+    monkeypatch.setattr(random, "choice", random.Random(0).choice)
+
+    def reject_full_enumeration(*args):
+        """Fail immediately if sampling tries to enumerate the entire space."""
+        pytest.fail("Large combination spaces must be sampled, not enumerated")
+
+    monkeypatch.setattr(itertools, "product", reject_full_enumeration)
+
+    combinations = optimizer._generate_combinations(populations)
+
+    assert len(combinations) == (sample_size or optimizer.population_size)
+    assert len({tuple(sorted(combo.items())) for combo in combinations}) == len(combinations)
+    for combo in combinations:
+        assert combo.keys() == populations.keys()
+        assert all(prompt in populations[node] for node, prompt in combo.items())
+
+
+def test_small_combination_space_is_enumerated(optimizer):
+    """Return the full Cartesian product when it fits within the sample size."""
+    populations = {"planner": ["plan_a", "plan_b"], "solver": ["solve_a", "solve_b"]}
+
+    combinations = optimizer._generate_combinations(populations)
+
+    assert combinations == [
+        dict(zip(populations, prompts)) for prompts in itertools.product(*populations.values())
+    ]
+
+
+def test_empty_node_population_has_no_combinations(optimizer):
+    """A workflow with an empty node population cannot produce a candidate."""
+    assert optimizer._generate_combinations({"planner": ["plan"], "solver": []}) == []