With the increase in computational capability, there is an increase in classes of engineering optimization problems that are considered solvable. Not all problems benefit from similar types of approaches when searching for an optimal solution. Some have objective functions that can be described as largely unimodal while others have complex behavior with multiple local optima. Further, there are problems that have behavior that is not clearly apparent due to the involvement of CAD/CAE tools and high number of inputs/factors. There has been a push to combine dissimilar optimization approaches in order to tackle such hard-to-solve problems for a variety of reasons. One such combination is the “Hybrid” optimization algorithm developed by ESTECO for their commercial optimization software “modeFRONTIER”.This paper gives the reader some examples and results from problems where the Hybrid algorithm has proved to be a worthy choice. Additionally, the algorithm has also been applied to cases that are representative of the optimization challenges in the automotive industry today. These include a few examples with CFD and FEA based design optimization problems. Since the Hybrid algorithm is a combination of derivative free Genetic Algorithm (GA) and derivative based Sequential Quadratic Programing (SQP), a major parameter that affects the performance of the algorithm is the percentage of SQP based designs that are generated during the optimization run. The results of varying this parameter are also discussed.The paper will also cover performance comparison between the Hybrid Optimization Algorithm and few other standard and popular algorithms for single and multi-objective problems.