This work presents the simultaneous synthesis, design and control of an activated sludge process using a Multivariable Model-based Predictive Controller (MPC). The process synthesis and design are carried out simultaneously with the MPC tuning to obtain the most economical plant which satisfies the controllability indices that measure the control performance (H∞ and l1 norms of different sensitivity functions of the system). The mathematical formulation results into a mixed-integer optimization problem with non-linear constraints that is solved using a real coded genetic algorithm. The solutions obtained evidence the effect of applying different bounds over the controllability norms considered. The results are encouraging for the development of integrated design approaches with advanced control schemes which usually results in complex optimization problems difficult to solve with conventional optimization techniques.