At present, methanol is mostly produced from syngas, derived from natural gas through steam methane reforming (SMR). In a typical methanol production plant, unreacted syngas is recycled for mixing with natural gas and both used as fuel in the reformer furnace resulting in carbon dioxide (CO₂) emissions from the flue gases emitted into the atmosphere. However, CO₂ can be captured and utilized as feedstock within the methanol synthesis process to enhance the productivity and efficiency. To do so, dynamic optimization approaches to derive the ideal operating conditions for a Lurgi type methanol reactor in the presence of catalyst deactivation are proposed to determine the optimal use of recycle ratio of CO₂ and shell coolant temperature without violating any process constraints. In this context, this study proposes a new approach based on a hybrid algorithm combining genetic algorithm (GA) and generalized pattern search (GPS) derivative-free methodologies to provide a sufficiently good solution to this dynamic optimization problem. The hybrid GA-GPS algorithm has the advantage of sequentially combining GA and GPS logics: GA, as the most popular evolutionary algorithm, effectively explore the landscape of the fitness function and identify promising basins of the search space, whereas GPS efficiently searches existing basins in order to find an approximately optimal solution. The simulation results showed that implementing the shell temperature trajectory derived by the proposed approach with 5% recycle ratio of CO₂ increased the production of methanol by approximately 2.5% compared to the existing operating conditions.