Enhancing the performance of existing distillation columns has received considerable attention from the chemical process industry; however, success depends strongly on the optimization step. The authors propose a practical method employing a particle swarm optimization (PSO) methodology for optimal retrofitting of existing distillation columns. The PSO algorithm allows a problem with multiple potential solutions to be solved by moving the particles around the search space via simple PSO methodology formulations. MATLAB was used to implement the algorithm and was then connected to an HYSYS model. Two industrial cases were examined in distillation retrofit optimization of the separation sequence for zeotropic and azeotropic mixtures to evaluate the efficiency of the PSO methodology. The results show that the methodology is competitive with conventional optimization techniques such as the response surface methodology, coordinate descent methodology, and genetic algorithm. Not only the structural variables but also the operating variables are simply and effectively optimized. Notably, the operating cost savings were calculated as 34.2% and 12.8% for ethylene dichloride purification and acetone-methanol separation processes, respectively. Furthermore, the reduction of carbon dioxide emission was investigated after retrofitting distillation sequences.