The belt conveyor is a transporting machine by friction in a continuous manner. The two order helical gearing reducer may be generally used as conveyor transmission, and can reduce speed and increase torque of belt. The objective function may be specified that that total center distance of the reducer incline to minimum, so the optimization model including the property and boundary constraints is created. Then the objective function with penalty terms is converted by penalty strategy with addition type, so as to transform the constrained optimization into the unconstrained optimization model. Considering the problem of low efficiency and local optimum caused by standard optimization methods, the simulated annealing algorithm is adopted to solve the optimization model of Belt Conveyor Transmission, and neural network method is applied to fit relative coefficient, then BFGS Quasi-Newton method is recalled automatically when the setting working precision is achieved again. So that the optimization process is simplified and global optimum is acquired reliably.