In the present study, a forced draft mechanical cooling tower has been experimentally investigated using trickle, film and splash fills. Various performance parameters such as range, tower characteristic ratio, effectiveness and water evaporation rate are first analyzed for each fill. Thereafter, based upon the experimental data, pertinent correlations have been developed for performance parameters by considering mass flow rates of water and air as design variables. Each of the performance parameters is considered to be an individual objective function and all objectives are then simultaneously optimized for maximizing the performance of the cooling tower using elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II). The multi-objective optimization algorithm gives a set of possible combinations of design variables, which is referred as the optimal Pareto-front, out of which a unique combination is selected based upon a decision making criterion. The proposed decision making procedure evaluates a Decision Making Score (DMS) based on assigned performance priorities for each point of the Pareto-front. Depending on DMS a unique combination of design variables is then selected for each type of fill that maximizes the tower's performance. These optimal points and the corresponding objective function are finally compared and based upon the highest DMS value, the wire-mesh (trickle) fill is found to be the most efficient fill under the present experimental conditions. The methodology presented in this work has been made more generalized, so that it can be easily implemented in industrial forced draft cooling tower operating under a wide range of temperatures. (C) 2016 Elsevier Ltd. All rights reserved.