Abstract Application problems have conflicting objectives and constraints, and maximum number of generations is the most common termination criterion in evolutionary algorithms used for solving these applications. This study develops a termination criterion using the non-dominated solutions obtained as the search progresses. For this, several performance metrics are modified, and their variation with generations has been assessed on many test functions. Based on this analysis, it is proposed to terminate the search if the improvement in variance of two selected performance metrics obtained in recent generations is statistically insignificant. Additionally, evaluation of objectives and constraints is computationally expensive in many applications. This study uses taboo list with multi-objective differential evolution to avoid re-visits and for better exploration of search space. Benefits of the termination criterion and taboo list are assessed on constrained benchmark problems. The developed approach is then evaluated on three chemical engineering applications, namely, alkylation, Williams-Otto and fermentation processes.