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In this article, evolutionary algorithms (EAs) are applied to solve the radio network design problem (RND). The task is to find the best set of transmitter locations in order to cover a given geographical region at an optimal cost. Usually, parallel EAs are needed to cope with the high computational requirements of such a problem. Here, we develop and evaluate a set of sequential and parallel genetic algorithms (GAs) to solve the RND problem efficiently. The results show that our distributed steady state GA is an efficient and accurate tool for solving RND that even outperforms existing parallel solutions. The sequential algorithm performs very efficiently from a numerical point of view, although the distributed version is much faster.