Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems in engineering. In addition to parameter optimization, genetic algorithms are also suggested for solving problems in creative design, such as combining components in a novel, creative way. Genetic algorithms (GA) transpose the notions of evolution in Nature to computers and imitate natural evolution. Basically, they find solution(s) to a problem by maintaining a population of possible solutions according to the 'survival of the fittest' principle. We present here the main features of GAs and several ways in which they can solve difficult design problems. We briefly introduce the basic notions of GAs, and discuss how GAs work. We then give an overview of applications of GAs to different domains of engineering design.