Vehicle restraint systems are optimized to maximize occupant safety and achieve high safety ratings. The optimization formulation often involves the inclusion or exclusion of restraint features as discrete design variables, as well as continuous restraint design variables such as airbag firing time, airbag vent size, inflator power level, etc. The optimization problem is constrained by injury criteria such as Head Injury Criterion (HIC), chest deflection, chest acceleration, neck tension/compression, etc., which ensures the vehicle meets or exceeds all Federal Motor Vehicle Safety Standard (FMVSS) requirements. Typically, Genetic Algorithms (GA) optimizations are applied because of their capability to handle discrete and continuous variables simultaneously and their ability to jump out of regions with multiple local optima, particularly for this type of highly non-linear problems. However, the computational time for the GA based optimization is often lengthy because of the relatively slow convergence comparing to derivative based algorithms. This study compares GA and multi-strategy optimization algorithms on driver’s side full frontal 90-degree rigid barrier impact MADYMO simulations at different impact speeds with belted and unbelted occupants. The multi-strategy optimization algorithms are sophisticated combinations of GA, gradient-based algorithms, and Response Surface Modeling (RSM). Design engineers are given conclusions and suggestions based on the comparison of optimization performance of aforementioned algorithms.