Portfolio optimization, also known as portfolio selection, is a crucial financial problem that involves allocating a limited budget across a set of assets. In this context, there are two investment strategies: active and passive. One of these passive strategies is known as the enhanced index tracking problem, a multi-objective NP-hard combinatorial optimization problem. Since evolutionary algorithms do not rely on convexity and differentiability assumptions and their population-based nature allows them to obtain many elements of the Pareto front in a single run, their use has gained popularity in the context of portfolio optimization. This study aims to compare the performance of eight state-of-the-art multi-objective evolutionary algorithms in solving the enhanced index tracking problem.