A stochastic-based simultaneous design and control methodology for chemical processes under uncertainty is presented. An optimization framework is proposed with the aim of achieving a feasible and stable optimal process design in the presence of stochastic disturbances while using advanced model-based control schemes such as Model Predictive Control (MPC). The key idea is to determine the dynamic variability of the system that will be accounted for in the process design using a stochastic-based worst-case variability index. This index is computed from the probability distribution of the worst-case variability of the process variables that determine the dynamic feasibility or the dynamic performance of the system under random realizations in the disturbances. A case study of an actual wastewater treatment industrial plant is presented and used to test the proposed methodology and compare its performance against the sequential design approach and a simultaneous design and control method using conventional PI-based control schemes. (C) 2014 Elsevier Ltd. All rights reserved.