Achievement scalarization function is one of the method for converting the multiobjective problem into a single objective one. The differences of this methodology among the similar scalarization approaches are its performance and utilization of the reference point set on objective space which is preferred on modern (especially many objective problems) optimization algorithms. Since it is possible to use various optimization algorithms with this scalarization method, in this study a classical (also well-known and relatively complicated) optimization algorithm as a part of nonlinear programming called sequential quadratic programming is preferred. The analysis initially is started by applying this method into two benchmark problems to show the performance of this scalarization function on convex and concave problems and then the idea is applied to optimize the patch antenna problem as a multiobjective real-world optimization problem. The results show that not only the satisfactory performance obtained from classical optimization algorithm but also the method allows the researchers to select different levels of the substrate thickness of the patch antenna which is a critical issue for joining the simulation results into implementation phase.