Abstract: The real-world optimization of dynamic processes, such batch processes, space applications and robotic problems, is usually a matter of several objectives and constraints. In many cases it is difficult to deal with such problems with conventional methods. Evolutionary methods provide an interesting alternative, with less programming and computational efforts. This paper presents four GA methods for solving a complex multiobjective problem that can serve as an illustrative example: the optimisation and control of the industrial beer fermentation. The first GA method is based on aggregating functions, and the other three adopt a Pareto set approach.