Evolutionary algorithms are direct stochastic search algorithms that simulate a Darwinian evolutionary system. This means that an evolutionary algorithm solves optimization problems by numerically simulating organic evolution. As any direct search method, evolutionary algorithms use minimal information about the fitness function. They do not require computing the gradient of the fitness function nor they need that the fitness function is unimodal or even continuous. Those characteristics make evolutionary algorithms a suitable approach to solving optimization problems that are discontinuous, noisy, multimodal, multi-objective etc. This chapter introduces the reader to evolutionary algorithms, describing and discussing their main features. The material assumes no previous knowledge about evolutionary algorithms