Inspired by the successful description of the first steps of molecular evolution by the quasispecies theory and the successful application of quasispecies-like algorithms to optimization problems, we propose a hierarchically organized algorithm. This new algorithm is able to solve a spin glass and a travelling salesman problem using only point mutations. Furthermore, it performs better under comparable circumstances than the ordinary quasispecies algorithm. Depending on the structure of the fitness landscape of the examined problem under consideration the hierarchically organized algorithm proves to be much more suitable than a simple quasispecies algorithm, especially in clustered landscapes. Tuning the error rates reveals the critical minimum copy fidelity necessary to guarantee optimization. We propose to incorporate hierarchical concepts into optimization algorithms inspired by biological evolution, such a genetic algorithms.