In my talk I'm going to present a quite efficient tool for optimization and constraint satisfaction problems. These methods are called evolutionary or genetic algorithms since their basic concepts mimic the evolution of species. These algorithms were first used to solve discrete problems like scheduling. Later the modified version proved efficient for a class continuous problems, where gradient method and its variants fail either because the objective function isn't differentiable, or the function has many local optima. An example for a specific continuous problem will be presented.