The elitist version of nondominated sorting genetic algorithm (NSGA II) has been adapted to optimize the industrial grinding operation of a lead-zinc ore beneficiation plant in mineral processing sector. Two objectives functions identified for this study are throughput of the grinding circuit and percent passing of one of the most critical size fractions (both are maximized). Simultaneously, it is also ensured that the grinding product meets all other quality requirements by keeping percent passing of two other size classes, percent solid and recirculation load of the grinding circuit within the user specified bounds (constraints). Solid ore and water flowrates to the circuit are treated as manipulated (decision) variables. Pareto set, for the conflicting objectives, has been generated. NSGA II is found to generate Pareto front significantly dense in terms of spread of optimal points and better in comparison with the Pareto front generated by other weight and constraint based approaches. © 2004 Elsevier B.V. All rights reserved.