Genetic algorithms (GAs) are a quite recent technique of optimization, whose basic concept is mimicking the evolution of a species, according to the Darwinian theory of the "survival of the fittest." The application of genetic algorithms to complex problems usually produces much better results than those obtained by the standard techniques. This paper explains in detail the different steps of the algorithm and the most relevant problems to be solved in order to obtain an efficient optimization tool.