This work presents a new methodology for the simultaneous process flowsheet and control design of dynamic systems under uncertainty using a model predictive control (MPC) strategy. Although several methodologies that include structural decisions in the analysis have been reported, only a few have considered a model-based control strategy such as MPC. An iterative decomposition framework that includes a dynamic flexibility analysis, a robust dynamic feasibility test, a nominal stability analysis, and a robust asymptotic stability test is presented for optimal process flowsheet selection. While previous methodologies have formulated the dynamic feasibility and stability analysis as a mixed-integer nonlinear programming (MINLP) problem, the present method formulates these analyses as convex problems for which efficient numerical algorithms exist. The simultaneous process flowsheet and MPC design method was tested on a system of Continuous Stirred Tank Reactors (CSTRs) with multiple inlet streams. The results show that the optimal design obtained by the present method remained feasible and asymptotically stable in the presence of the critical realizations in the disturbances. Comparisons between the designs obtained by the present MPC-based method and those obtained with other design approaches, i.e., optimal steady-state design and simultaneous design and control using a multiloop proportional and integral (PI) control scheme, are presented and discussed in this work.