Search-based testing generates test cases by encoding an adequacy criterion as the fitness function that drives a search-based optimization algorithm. Genetic algorithms have been successfully applied in search-based testing: while most of them use adequacy criteria based on the structure of the program, some try to maximize the mutation score of the test suite.This work presents a genetic algorithm for generating a test suite for mutation testing. The algorithm adopts several features from existing bacteriological algorithms, using single test cases as individuals and keeping generated individuals in a memory. The algorithm can optionally use automated seeding when producing the first population, by taking into account interesting constants in the source code.We have implemented this algorithm in a framework and we have applied it to a WS-BPEL composition, measuring to which extent the genetic algorithm improves the initial random test suite. We compare our genetic algorithm, with and without automated seeding, to random testing.