Computational material discovery can search large design space to identify promising candidates for experimental material design. Density Functional Theory (DFT) based first principle calculation has been able to calculate many electrical and physical properties of materials, making them suitable for computational doping based material discovery. In material doping, given a base material, one can change its properties by substituting some elements with new ones or adding additional elements. In computational doping, we have a grid of atoms in a supercell, some of which can be substituted with dopant atoms. For each such substitution, we use the Vienna Ab-Initio Simulation Package (VASP) to calculate its physicochemical properties, which takes about 30 hours for a grid of 75 atoms. This is a typical optimization problem with expensive evaluation functions. Here we developed a genetic algorithm for finding the most stable structure of the doped material with the lowest free electronic energy. It can reduce the running time for computational doping by up to 70%. We used SrTiO3 perovskite as the base material and Nb as the substitution element. Copyright is held by the owner/author(s).