Learning in neural networks is an optimization process by which the error function of a network is minimized. Any suitable numerical method can be used for the optimization. Therefore it is worth having a closer look at the efficiency and reliability of different strategies. In the last few years genetic algorithms have attracted considerable attention because they represent a new method of stochastic optimization with some interesting properties [163, 305]. With this class of algorithms an evolution process is simulated in the computer, in the course of which the parameters that produce a minimum or maximum of a function are determined. In this chapter we take a closer look at this technique and explore its applicability to the field of neural networks.