The objective of this research is to develop an energy-efficient and sustainable synthesis/separation process for the production of polyolefin elastomers, as well as identify the optimal operating conditions for this process. The proposed approach involves integrating additional reactors into the existing reactor network and implementing energy-saving strategies into the distillation process. To optimize the operating conditions, we employed three different meta-heuristic optimization techniques: non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), multi-objective particle swarm optimization, and multi-objective grey wolf optimizer. These techniques are utilized to simultaneously minimize total annualized cost (TAC), CO2 emissions, and condition number. To enhance the efficiency of these algorithms, we introduced the population distribution factor as a metric to enhance computational efficiency without compromising problem-solving aptitude. The results demonstrate that the improved algorithms offer enhanced computational efficiency and remarkable problem-solving ability and that NSGA-II outperforms the other algorithms in practical engineering problems. The proposed synthesis/separation sequences can reduce TAC and CO2 emissions simultaneously, while maintaining good controllability.