In this paper a nonlinear mathematical programming model is formulated and is solved by an innovative stochastic optimization algorithm, Simulated Annealing. The model is designed for simultaneous optimization of all variables that determine the design and construction as well as the operating costs of an SMB system. At the core of this model are the standing wave equations, which are algebraic relationships that offer a significant modeling advantage in optimizing SMB systems. Specifically, by solving these equations the desired purity and yield for nonideal systems is guaranteed. They also provide computational efficiency and tractability by eliminating numerical solutions of ordinary or partial differential equations. The method introduced in this paper, the Standing Wave Annealing Technique (SWAT), will extend the applicability of the standing wave equations to solve efficiently any single or multiobjective optimization problem for binary SMB systems with linear adsorption isotherms. Two examples from the literature are used in this study to illustrate the flexibility and computational efficiency of SWAT: (1) the separation of glucose from sulfuric acid and (2) the separation of insulin from an impurity. The optimization results obtained are similar or superior to those previously reported. The computational time for SWAT, compared to a grid search method, is at least an order of magnitude shorter. This study shows that SWAT has the capability to efficiently solve wide range of SMB optimization problems.