This chapter introduces Evolutionary Algorithms which are a set of optimization and machine learning techniques that find their inspiration in the biological processes of evolution established by Darwin and other scientists in the ninenteenth century. Starting from a population of individuals that represent admissible solutions to a given problem through a suitable coding, these metaheuristics leverage the principles of variation by mutation and recombination, and of selection of the best-performing individuals in a given environment. By iterating this process the system finds increasingly good solutions and generally solves the problem satisfactorily. In this chapter Genetic Algorithms are discussed starting from basic concepts and examples up to a detailed description of their inner working.