Evolutionary strategies in general and genetic algorithms in particular have turned out to be of increasing relevance for various classes of optimization problems like combinatory problems as a discrete example or shape optimization as a continuous example. In this paper, we present efficient and powerful strategies for genetic algorithms and their application to two classes of optimization problems. Besides algorithmic aspects concerning the genetic essentials, the focus is put on the efficient implementation, both of the sequential and of the parallel versions.