Portfolio optimization is a crucial practice in finance aimed at maximizing the return while minimizing the risk through strategic asset allocation. This paper explores two distinct approaches to modeling robust portfolio optimization, comparing their efficacy in balancing the return and the risk. The first approach focuses on diversifying the portfolio by varying the number of stocks and sector allocation, while the second approach emphasizes minimizing risk by selecting stocks with low correlation. Theoretical foundations and mathematical formulations underpinning these approaches are discussed, incorporating concepts from Modern Portfolio Theory and Mixed Integer Linear Programming. Practical implementation involves data collection from Yahoo Finance API and computational analysis using Python and the optimization tool Gurobi. The results of these methodologies are evaluated, considering factors such as budget constraints, maximum and minimum investment limits, binary constraints, and correlation thresholds. The study concludes by discussing the implications of these findings and their relevance in contemporary financial decision-making processes.