A hybrid optimization framework is introduced to identify enzyme sets and levels to meet overproduction requirements using kinetic models of metabolism. A simulated annealing algorithm is employed to navigate through the discrete space of enzyme sets while a sequential quadratic programming method is utilized to identify optimal enzyme levels. The framework is demonstrated on a model of E.coli central metabolism for serine biosynthesis. Computational results show that by optimally manipulating relatively small enzyme sets, a substantial increase in serine production can be achieved. The proposed approach thus provides a versatile tool for the elucidation of controlling enzymes with implications in biotechnology.