Abstract In this chapter we describe evolutionary algorithms (EAs) for constraint handling. Constraint handling is not straightforward in an EA because the search operators mutation and recombination are “blind” to constraints. Hence, there is no guarantee that if the parents satisfy some constraints the offspring will satisfy them as well. This suggests that the presence of constraints in a problem makes EAs intrinsically unsuited to solve this problem. This should especially hold when the problem does not contain an objective function to be optimized, but only constraints – the category of constraint satisfaction problems. A survey of related literature, however, indicates that there are quite a few successful attempts to evolutionary constraint satisfaction. Based on this survey, we identify a number of common features in these approaches and arrive at the conclusion that EAs can be effective constraint solvers when knowledge about the constraints is incorporated either into the genetic operators, in the fitness function, or in repair mechanisms. We conclude by considering a number of key questions on research methodology.