An optimized strategy for simulation and optimization of steady-state processes, under an equation-oriented environment, is presented. Equation-oriented environments apply a solution procedure to solve the entire system of non-linear algebraic equations arising from the mathematical model describing these processes. The difficulty in solving these systems may change drastically by specifying different independent variables for the degrees of freedom, as it has long being recognized. An algorithm that chooses the decision variables by minimizing the number and size of the subsystems of equations that need to be simultaneously solved for, while allowing for the inclusion of functional constraints, is used (Salcedo and Lima, 1999). With this algorithm, optimum sets of decision variables and the corresponding solution strategies are obtained. This paper describes the implementation of this approach linked with a simulated annealing-based global optimizer. The proposed strategy was applied to the optimization of a reactor-extractor system and to a more difficult absorber-stripping system with heat integration. With these examples we pretend to compare different optimization procedures for each test case, respectively solving the entire system of equations, solving some smaller subsystems (a local optimum for the simulation step) or solving for the global optimum of the simulation step (which may correspond to a sequential solution). By optimizing the simulation step much more accurate results as well as significantly reduced CPU times are obtained, in comparison with simultaneous solution strategies, suggesting that this may be a powerful tool for global optimization.