Computerized crash reconstruction of real world crashes involves dealing with a lot of unknown parameters and as such the reconstruction problem cannot be solved deterministically as was shown using a parametric methodology presented in our previous ESV paper titled “Computational Analysis of Real World Crashes: A Basis for Accident Reconstruction Methodology.” This paper introduces a modified version of the parametric methodology, which involves using an optimization scheme to derive an optimal solution for the reconstruction problem in a given range of unknown parameters. Real world crashes were selected from the CIREN database and were solved using the proposed methodology. Human-Vehicle-Environment (HVE) software was used to generate the crash pulse where EDR data were missing. The problem was set up in MADYMO. During the set up, the unknown parameters were identified. ModeFRONTIER software was used for optimization. The identified unknown parameters were treated as design variables. The objective function and the constraints were defined such that they minimize the differences in injuries and occupant-vehicle contacts between the real world data and the model prediction. Since the objective function has a great effect on the final solution, a normalized form of the objective function, weighted based on the AIS level of the injuries sustained by the occupant, was formed in this study. A genetic algorithm with Sobol DOE (Design of Experiments) was used for optimization. Results of the simulations showed that the optimal solution correctly predicted both the occupant-vehicle contacts and the injuries sustained by the occupant. By viewing the occupant motion inside the vehicle during the crash, better occupant protection systems can be devised. Correlation studies were also carried out to find the critical parameters affecting the solution. In addition, a best case scenario study was carried out to find, using optimization, the design changes that could help mitigate all or some of the injuries sustained by the occupant.