The optimization of processes described by non-linear distributed parameter systems is investigated. In general, the complexity of such a problem prevents the implementation of classical deterministic optimization methods. In fact, for global optimization problems as well as for problems involving very large number of variables for which the reduction of the computational time is crucial, these methods can be inefficient. In this framework, several minimization algorithms (deterministic, stochastic, evolutionary) are compared. An application to optimal control determination is presented for a class of distributed parameter systems called spreadable systems.