An application of a decomposition approach for large-scale optimization (i.e., the Iterative Local-Global Optimization (ILGO) approach) developed by Muñoz and von Spakovsky (2001) is presented. The synthesis / design optimization of a turbofan engine coupled to an environmental control system for a military aircraft was carried out. The problem was solved for a given mission (i.e. a load / environmental profile) composed of fifteen segments. The number of decision (independent) variables both discrete and continuous for this highly non-linear optimization problem was one hundred fifty-three. Both the thermodynamic and physical (weight and volume) simulations were carried out using state-of-the art tools. Three objective functions were investigated: take-off gross weight, mission fuel consumption and total cost, and no observable differences were found in the final results. In addition to the mathematical foundations for global convergence presented in Muñoz and von Spakovsky (2000b, 2001), convergence was validated numerically by solving the entire mixed-integer non-linear programming (MINLP) problem without decomposition using a subset of the independent variables. The constant value of the shadow prices (or linear behavior of the Optimum Response Surface – OSR) played a major role in global convergence of the ILGO approach.