The real-world optimisation of dynamic processes, such as 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 Evolutionary methods for solving complex multiobjective problems applied to an illustrative example: the optimisation and control of the industrial beer fermentation. The first method is based on aggregating functions, and the others adopt a Pareto set approach.