Classic optimization methods are bound to have many limitations. As a result, such methods are of ten not suitable for efficient problem solving. This paper puts forth aproposal for a new hybrid optimization method which combines together two basic methods, i.e. Monte Carlo method and Rosenbrock method. The combination produces a method that has all of its constituents' advantages, yet does not in herit any oft heir drawbacks, resulting in higher convergence rates and greater computation speeds. Due to its simplified approach towards modeling, our method can be easily adapted to parallel or distributed computing systems, enabling researchers to use clusters consisting of many separate machines. Those clusters can provide the computational power needed to solve complicated optimization problems..