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To better meet the needs of crop growth and achieve energy savings and efficiency enhancements, constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved. In this work, a radial-basis function (RBF) neural network was used to mine the potential changes of a greenhouse environment, a temperature error model was established, a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II). The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature. The NSGA-Ⅱ could well search for the Pareto solution for the objective functions. The experimental results showed that after 40 min of combined control of sunshades and sprays, the temperature was reduced from 31℃ to 25℃, and the power consumption was 0.5 MJ. Compared with the three days of July 24, July 25 and July 26, 2017, the energy consumption of the controlled production greenhouse was reduced by 37.5％, 9.1％ and 28.5％, respectively.