Many uncertain factors affect the operation of wastewater treatment plants. These include the physical and chemical properties of wastewater streams as well as the degradation mechanisms exhibited by biological processes. Because of the rising concerns about environmental and economic impacts, improved process control algorithms, using artificial intelligence technologies, have received wide attention. Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, and neural networks, may provide improved control of effluent quality. The methodology proposed in this study employs a three-stage analysis that integrates three soft computing approaches for generating a representative state function, searching a set of multiobjective control strategies, and autotuning the fuzzy control rule base used for controlling a treatment plant. The case study, using an industrial wastewater treatment plant in Taiwan as an example, demonstrates the applicability of the approach. The findings from this research suggest that a genetic-algorithm-based hybrid fuzzy-neural controller can produce better plant performance than does a simple fuzzy logic controller, in terms of both environmental and economic objectives. This methodology can be extended to control many other types of wastewater treatment processes, as well, by making only minor modifications.