Many modern automated test generators are based on either meta-heuristic search techniques or use constraint solvers. Both approaches have their advantages, but they also have specific drawbacks: Search-based methods get stuck in local optima and degrade when the search landscape offers no guidance; constraint-based approaches, on the other hand, can only handle certain domains efficiently. In this paper we describe a method that integrates both techniques and delivers the best of both worlds. On a high-level view, our method uses a genetic algorithm to generate tests, but the twist is that during evolution a constraint solver is used to ensure that mutated offspring efficiently explores different control flow. Experiments on 20 case study examples show that on average the combination improves branch coverage by 28% over search-based techniques and by 13% over constraint-based techniques.