Optimization in chemical engineering often involves two or more objectives, which are conflicting. Multiobjective optimization (MOO) generates a set of equally good solutions from the perspective of objectives used; these solutions are known as nondominated or Pareto-optimal solutions. Although MOO has become popular in chemical engineering in the past 20 years, majority of studies are limited to finding Pareto-optimal solutions and only some papers discussed the selection of one nondominated solution for implementation. Thus, the main research gap in the application of MOO in chemical engineering is the very limited use of selection or ranking methods. To address this gap, 8 weighting methods and 4 more selection methods (besides 10 methods analyzed by Wang and Rangaiah) are carefully chosen and implemented in an MS Excel-based program. Then, all these methods are applied to 12 mathematical and 13 chemical engineering problems. The results of this extensive analysis indicate that the entropy method, criteria importance through intercriteria correlation method, and best-worst method are better for determining weights for objectives. Further, out of the four additional selection methods tested, multiattributive border approximation area comparison is suggested for ranking Pareto-optimal solutions.