An algorithm (M-SIMPSA) suitable for the optimization of mixed integer non-linear programming (MINLP) problems is presented. A recently proposed continuous non-linear solver (SIMPSA) is used to update the continuous parameters, and the Metropolis algorithm is used to update the complete solution vector of decision variables. The M-SIMPSA algorithm, which does not require feasible initial points or any problem decomposition, was tested with several functions published in the literature, and results were compared with those obtained with a robust adaptive random search method. For ill-conditioned problems, the proposed approach is shown to be more reliable and more efficient as regards the overcoming of difficulties associated with local optima and in the ability to reach feasibility. The results obtained reveal its adequacy for the optimization of MINLP problems encountered in chemical engineering practice.