Die wear is regarded as a crucial factor which affects die life and quality of products. In hot extrusion process, finite-element method (FEM), BP neural network and genetic algorithm were combined together to optimize extrusion die profile which yielded more uniform wear depth distribution on die profile. A method of B-spline function interpolation was used to describe extrusion die profile. The temperature, pressure and velocity field of nodes that lied on extrusion die profile were gained by FEM simulation. Wear depth of extrusion die profile was calculated by modified Archard theory. The results were used to train BP neural network, so that nonlinear mapping relations between reference points of die profile and wear depth were obtained. In order to gain uniform wear depth, genetic algorithm was applied to optimize extrusion die profile. Optimum result, compared with common conical die profile, reduced wear depth of extrusion die and improved service life. At the same time, the optimal result accorded with practical conditions.