In the literature, the available techniques for MPC tuning usually consider a specific operating point (OP), while in real plants, controllers should be robust in a wide operating region facing different plant behaviors that arise due to disturbances, saturations, and nonlinearities. In this work, a method for MPC tuning proposed in our previous work is extended for a robust tuning for classical (square) MPCs. This technique applies to any predictive control algorithm, and it considers multi-scenarios based on the closed-loop attainable performance of the system. The sequential procedure is applied, where initially the attainable performance for each scenario (herein, different OPs are used) is determined, and an estimate of the closed-loop potential is computed. In the end, the optimum scaling for the model and the MPC tuning parameters are calculated, solving an optimization problem that uses the attainable trajectories for each scenario as a reference. The selection of the controller's process model is also determined according to constraints of the attainable performance determination. This robust and constrained strategy is applied to the controller design of two nonlinear systems: (i) the quadruple-spherical tank system and (ii) a continuous stirred tank reactor with separation column and recycle. The proposed approach was successful in tuning the MPC capable of working with all proposed OPs, including those with critical operational conditions for the process controllability, such as nonminimum phase dynamics and model-plant mismatches.