We implemented a fragment-based de novo design algorithm for a population-based optimization of molecular structures. The concept is grounded on an evolution strategy with mutation and crossover operators for structure breeding. Molecular building blocks were obtained from the pseudo-retrosynthesis of a collection of pharmacologically active compounds following the RECAP principle. The influence of mutation and crossover on the course of optimization was assessed in redesign studies using known drugs as template structures. A topological atom-pair descriptor grounded on potential pharmacophore points was used as a molecular descriptor, and the Manhattan distance between the template and candidate molecules served as a fitness function. Exclusive use of the crossover operator yielded few unique compounds and often resulted in premature convergence of the optimization process, whereas exclusive use of the mutation operator resulted in diverse high-quality structures. Combinations of crossover and mutation yielded the overall best results. The majority of the designed structures exhibit a chemically reasonable architecture; chiral centers are rare, and unfavorable connections of building blocks are infrequent. We conclude that this fragment-based design principle is suited as an idea generator for the automated design of novel leadlike molecules.