The design of a process determines the properties of variable interaction, disturbance rejection, and set-point tracking which comprise its inherent controllability. Process synthesis usually focuses on optimizing an economic objective without consideration of controllability. This paper outlines and illustrates a systematic procedure for incorporating open-loop steady-state controllability measures within the mathematical programming approach of process synthesis. This approach translates a superstructure of design alternatives into a multiobjective mixed-integer nonlinear (MINLP) optimization problem. With the flowsheet unknown a priori, the formulation requires expressions for the steady-state economic and control objectives in terms of the unknown design variables. Use of the epsilon-constraint method within the framework of the Generalized Benders Decomposition algorithm generates the noninferior solution set. A multiobjective optimization algorithm based on cutting planes determines a best-compromise solution among the various design and control objectives using as trade-off weights the partial derivative information from the noninferior set. The proposed procedure is then applied in binary distillation synthesis to consider multiple objectives for the RV control configuration, to examine different control configurations, and to deal with heat-integrated columns. Closed-loop dynamic simulations compare the responses of columns identified in the optimization.