This study presents a numerical optimization to optimize an axial flow fan blade to increase the efficiency. The radial basis neural network is used as an optimization method with the numerical analysis by Reynolds-averaged Navier-Stokes equations using SST model as turbulence closure. Four design variables related to airfoil maximum camber, maximum camber location, leading edge radius and trailing edge radius, respectively, are selected, and efficiency is considered as objective function which is to be maximized. Thirty designs are evaluated to get the objective function values of each design used to train the neural network. Optimum shape shows the efficiency increased by 1.0%.