The conversion of syngas derived from natural gas into methanol has been considered a relatively clean and environmentally friendly process. However, carbon dioxide is emitted as a result of using natural gas as fuel in the reformer furnace combustion zone to supply the heat required for endothermic reforming reactions. Carbon dioxide is a primary greenhouse gas emitted as flue gas from the reformer and has been contributing to global warming over the past few decades. Thereby, environmental regulations for new and existing industrial facilities have been enforced to mitigate the adverse effects of carbon dioxide emission. In this research, multiobjective optimization is applied for the operating conditions of the methanol synthesis loop via a multistage fixed bed adiabatic reactor system with an additional interstage CO2 quenching stream to maximize methanol production while reducing CO2 emissions. The model prediction for the methanol synthesis loop at steady state showed good agreement against data from an existing commercial plant. Then, the process flowsheet was developed and fully integrated with the Genetic Algorithms Toolbox that generated a set of optimal operating conditions with respect to upper and lower limits and several constraints. The results showed methanol production was improved by injecting shots of carbon dioxide recovered from the reformer at various reactor locations.