This study compares two multi-objective optimization algorithms, NSGA-II and Differential Evolution, for portfolio optimization. Evaluating their performance across various market conditions using return, risk, Sharpe ratio, diversification, and computational efficiency, we found that NSGA-II excels in diversification and risk management, particularly in volatile markets, while Differential Evolution performs better in stable conditions by maximizing returns and achieving high Sharpe ratios. These findings highlight the importance of selecting the appropriate algorithm based on market dynamics and investment goals, demonstrating NSGA-II’s robustness and Differential Evolution’s efficiency in different scenarios.