The portfolio optimization problem uses mathematical approaches to model stock exchange investments. Its aim is to find an optimal set of assets to invest on, as well as the optimal investments for each asset. In the present work, the problem is treated as a multi-objective optimization problem. Three well-known optimization techniques greedy search, simulated annealing and ant colony optimization are adapted to this multi-objective context. Pareto fronts for five stock indexes are collected, showing the different behaviors of the algorithms adapted. Finally, the results are discussed.