An optimization methodology based on neural networks and genetic algorithms was developed and used to optimize a real world process — an electro-coagulation process involving three pollutants at different concentrations: kaolin (250–1000 mg L−1), Eriochrome Black T solutions (50–200 mg L−1), and oil/water emulsion (1500–4500 mg L−1). Feed-forward neural networks using heterogeneous combination of transfer functions were developed, leading to good results in the validation stage (relative error about 8%). The parameters of the process (concentration of pollutant, time, pH0, conductivity and current density) were optimized handling the genetic algorithm parameters, in order to obtain a maximum removal efficiency for each pollutant. Therefore, the optimization methodology combines neural networks as modeling tools with genetic algorithms as solving method. Validation of the optimization results using supplementary experimental data reveals errors under 11%.