A fuzzy logic controller (FLC) for the dissolved oxygen (DO) concentration of a wastewater bioreactor is presented. The FLC is developed and tested based on simulations using first order plus dead time models obtained from experiments with an actual wastewater bioreactor. The FLC uses feedback of the error in DO concentration and rate of change of the DO concentration and manipulates the stem position of the flow control valves (FCVs) supplying air to the bioreactor. The proposed FLC is tested for robustness across several process models, two of which include proposed worst-case process conditions. The performance of the proposed hand tuned FLC is compared to that of a similarly tuned proportional-integral-derivative controller. The FLC is implemented as a lookup table for speed and ease of deployment. The disturbances present in the experimental step testing data are characterized and used as the basis for disturbing the control loop during controller performance testing. A low-pass filter is then included to subsequently smooth the feedback signal. The nonlinear relationship between the FCV stem position and output flow is modelled and included in the controller performance testing. A genetic algorithm (GA) is developed that manipulates the membership functions of the FLC to yield an optimal controller for the ensemble of process models. The ability of the GA to converge on an optimal FLC is verified through repeated trials. The performance of the GA optimized FLC is observed under realistic process conditions and is benchmarked against a manually optimized PID controller.