Optimization of catalyst composition using a genetic algorithm (GA) is intended to increase the activity in a series of repetitive steps consisting of determination of the composition, catalyst preparation, activity test and feedback to the program. The laborious steps of catalyst preparation and activity test can be replaced by calculation provided that a radial basis function network (RBFN) trained using experimental results is used to evaluate the fitness of the catalyst code.Optimization of the Cu/Zn/Al/Sc ratio of mixed oxide catalyst for methanol synthesis from syngas was simulated. In the simulation, activity was calculated by equations fitted to some experimental results to evaluate the fitness for use in the genetic algorithm program. Data of catalyst composition for input and the STY for output totalling 69-92 points were necessary for successful mapping of the catalytic activity. The network then was trained using 92 experimental results. The highest activity of the catalyst optimized by GA and RBFN was higher than that optimized by GA only. The combination of catalyst design by genetic algorithm and the activity evaluation by RBFN is promising for highly efficient catalyst screening.