In the real world, there is a wide amount of optimization issues, in which classical methods of mathematics programming can't guarantee the most optimum solution. Evolutional algorithms are metaheuristics. Over the last years have become more popular due to their conceptual simplicity and their efficiency in this type of issues. These algorithms have been used successfully in the area of multi-objective optimization, because of their nature. Based in populations demos, which allows generating several solutions from the conjuncts of optimums in one execution. Unfortunately, when the objective functions is computer costly to assess, the evolutional algorithms become impractical. In this paper we present a hybrid algorithm based in Pareto and Colony of Ants for the optimization of combinatory issues Bi-Objective.