A micro-evolutionary algorithm based on differential evolution (MiDE) is used to carry out a fast and effective exploration of the conformational space of molecular clusters (with dimensions about 8 A), which can be considered nanoparticle or nanoparticle scaffolds, finding and optimizing the minimum energy conformers. The search of the best geometry takes advantage of MiDE to perform good exploration over search space starting with a few candidate solutions. The presented software in this contribution, Micro-differential evolution cluster-optimizer (MiDECO), automatizes such exploration process. The Gaussian 09 package is used to perform the geometric optimizations of the clusters to compare the different conformations through the computed energies, which allowed the most stable molecules to be discerned. The software presented works for clusters of tested sets, reproducing the results reported in the state of the art and obtaining a significant reduction of the computational cost due to the micro-population used with the MiDE without using high-performance computing.