Domestic and industrial pollutions affected the water quality to a greater extent. Polluted water became a major reason behind several community diseases, mainly in undeveloped and developing countries. The public health condition is deteriorating and putting an extra burden of countermeasures to prevent such water borne diseases from spreading. Detecting the drinking water quality can prevent such scenarios prior to the critical stage. Recent research works have achieved reasonable success in predicting the water quality. However, the accuracy levels of already proposed models are to be improved, keeping in mind the sensitivity of the problem domain. In the current work, multi-objective genetic algorithm was employed to train the artificial neural network (NN-MOGA) to improve its performance over its traditional counterparts. The proposed model gradually minimizes two different objective functions; namely the root mean square error (RMSE) and Maximum Error in order to find the optimal weight vector for the artificial neural network (ANN). The proposed model was compared with three other, well established models namely NN-GA (ANN trained with Genetic Algorithm), NN-PSO (ANN trained with Particle Swarm Optimization) and SVM in terms of accuracy, precision, recall, F-Measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows index (FM index). The simulation results established superior accuracy of NN-MOGA over the other models. © 2017 IEEE.