A model has been developed based on the available experimental data for propylene polymerization with long-chain branching. The system considered is a bicatalyst system, where the first catalyst produces the atactic polypropylene macromonomer and second one grafts the atactic polypropylene macromonomers to isotactic polypropylene backbone. Pareto optimal solutions for long-chain branched polypropylene with the binary catalyst system are obtained by adapting nondominated sorting genetic algorithm for a particular multiobjective setup. Optimization objective is to produce polymer of maximum weight average molecular weight and maximum grafting density (expressed as number of macromonomers per 1000 backbone monomer units) in minimum polymerization time. Two catalysts and one cocatalyst concentration, second catalyst addition time are taken as decision variables with relevant process constraints that take care of model validity over prescribed operating range. A wide variety of process choices have been obtained for the optimization setup which shows betterment in process performance (e.g., ∼17% improvement in grafting density and 1% improvement in weight average molecular weight with the same polymerization time attained as compared to the processes reported in the literature Ye and Zhu, 2013). POLYM. ENG. SCI., 2014. © 2014 Society of Plastics Engineers