An improved genetic algorithm approach, based on a new ranking strategy, has been proposed to conduct multi-objective optimization of chemical engineering problems. New operators have been introduced to enhance the algorithm performance and reduce the computational effort. A Pareto-set filter operator has been implemented to avoid missing Pareto optimal points during the evolutionary process. A niche operator has been adopted to prevent genetic drift, and an elitism operator, to insure the propagation of the best result of each objective function. A fitness function based on each rank population size and rank level has been used to determine the reproduction ratio. Constraints are handled through a fuzzy penalty function method. The algorithm has been applied to a batch free-radical styrene polymerization process in order to maximize the monomer conversion rate and minimize the concentration of initiator residue in the product. The algorithm proved to be robust, handling satisfactorily multi-modal and multidimensional problems. (C) 2003 Elsevier Science Ltd. All rights reserved.