EvolveNav introduces a self-evolving framework for zero-shot object-goal navigation that improves during test time. It uses a rule memory derived from past trajectories and a confidence-based retrieval strategy to select effective actions, reducing redundant exploration. The method achieves a 10.1% higher success rate than existing baselines with fewer unnecessary steps.