The growing interest in intensified process units that improve efficiency by combining several phenomena into one unit, has led to a loss in degrees of freedom when addressing the control scheme of these units. Previous work demonstrated that a novel module-based design approach to membrane reactors could improve the operability index of membrane reactor systems. This approach sought to decouple the phenomena to regain some degrees of freedom for the control system. However, the computational time to determine such an optimal module design made this class of design problems intractable to solve in a reasonable amount of time. This work proposes a set of design heuristics for a new module-based design approach for membrane reactors. These heuristics are used in combination with a genetic algorithm formulation to produce a novel, two-staged algorithm for the design and control of membrane reactor systems. This algorithm is developed in Python and uses rigorous membrane reactor models built in AVEVA Process Simulation. The proposed algorithm solves the original non-polynomial (NP) complexity problem in polynomial time (P), while still being able to find the optimal designs discovered in previous work through exhaustive methods.