To achieve an optimal balance between energy consumption (EC) and effluent quality (EQ) of a wastewater treatment process (WWTP), an intelligent optimal control strategy is proposed based on constrained barebones multiobjective particle swarm optimization algorithm (CBBMOPSO). First, a datadrivenbased fuzzy neural network prediction model of EC and EQ is constructed utilizing the process date measured from WWTP. Then, the proposed CBBMOPSO with adaptive disturbance is used for dynamically optimizing the setpoints of dissolved oxygen SO concentration and nitrate nitrogen SNO level. Furthermore, an intelligent decisionmaking system based on fuzzy membership function is designed to identify the optimal setting value from the Pareto optimal set. Finally, the optimization setpoints of SO and SNO are tracked by a fuzzy logic controller to realize multiobjective optimal control of the WWTP. The experimental results based on the international benchmark simulation model No.1 (BSM1) demonstrate that the proposed CBBMOPSO method can significantly reduce the energy consumption on the premise of assuring water quality.