This study presents a comparative analysis of two multi-objective optimization algorithms, NSGA-II and DEoptim, in the context of portfolio optimization. The research evaluates the performance of these algorithms based on key portfolio indicators such as return, risk, Sharpe ratio, diversification, and computational efficiency. The findings offer valuable insights into the trade-offs between computational speed and portfolio performance, highlighting the significance of algorithm selection in financial optimization scenarios.