Music classification continues to be an important component of music information retrieval research. An underutilized tool for improving the performance of classifiers is feature weighting. A major reason for its unpopularity, despite its benefits, is the potentially infinite calculation time it requires to achieve optimal results. Genetic algorithms offer potentially sub-optimal but reasonable solutions at much reduced calculation time, yet they are still quite costly. We investigate the advantages of implementing genetic algorithms in a parallel computing environment to make feature weighting an affordable instrument for researchers in MIR.