The transition to a sustainable energy system requires the development of alternative pathways that can effectively reduce greenhouse gas emissions while ensuring economic feasibility. Among these, e-fuels represent a promising solution, with e-methanol standing out as a particularly important synthetic fuel. Produced from renewable hydrogen and captured CO2, e-methanol has gained increasing attention due to its potential to support decarbonization efforts across chemical and energy sectors. This Master of Science thesis focuses on the optimization of e-methanol production processes employing mixed integer-linear programming (MILP) techniques to minimize the net present value of all the costs the system incurs over its lifetime to achieve higher efficiency while minimizing energy consumption.