In this paper, we introduce two new multiobjective optimization techniques based on the genetic algorithm (GA), and implemented as part of a multiobjective optimization tool called MOSES (Multiobjective Optimization of Systems in the Engineering Sciences). These methods are based in the concept of min-max optimum, and can produce the Pareto set and the best trade-off among the objectives. The results produced by these approaches are compared to those produced with other mathematical programming techniques and GA-based approaches using two engineering design problems, showing the new techniques' capability to generate better trade-offs than the approaches previously reported in the literature.