This paper presents a data-mining-based beam pumping unit process modeling and parameters optimization method to solve the problem of inefficiency and energy-intensive of beam pumping unit. The ideality of process parameters is one of the main factors influencing system efficiency and energy consumption, while the effectiveness of mode plays a key role in process parameters choosing. Beam pumping unit system is a complicated nonlinear system, and is hardly to be precisely described by precise mathematical models. Generalized regression neural network (GRNN), which is powerful in nonlinear mapping and generalization, is suitable for nonlinear systems. Therefore, GRNN is proposed to model the beam pumping unit in this paper, and the experimental results show that the fitness is good. Then the trained model is applied to optimize the decision parameters by vector evaluated particle swarm optimization based on Pareto (VEPSO-BP), and at last the resulting parameters are applied to the production. Experimental results show that after using the optimal parameters, the efficiencies and energy consumptions increase more than 6.6% and decrease more than 4.1% respectively, which illustrates the feasibility and effectiveness of the proposed method.