Systematic and simultaneous optimization of a collection of objectives is called multiobjective or multicriteria optimization. These sorts of optimization procedures are becoming commonplace in fields involving engineering design, process and system optimization. In this work, the multiobjective (MO) optimization of the bioactive compound extraction process was carried out. Using the Normal Boundary Intersection (NBI) approach the MO optimization problem is transformed into a weighted form called the beta-subproblem. This subproblem is then solved using two evolutionary strategies (differential evolution (DE) and genetic algorithm (GA)). Using these evolutionary strategies, the solutions to the extraction process which form the efficient Pareto frontier was generated. The Hypervolume Indicator (HVI) was applied to the solutions to rank the strategies based on the solution quality. Critical analyses and comparative studies were then carried out on the strategies employed in this work and that from the previous work. © Springer International Publishing Switzerland 2015.