Environmental pollution restrictions have forced car manufacturers to pay extra attention to exhaust after-treatment solutions. Current methods employ a three-way catalyst (TWC), which converts hazardous exhaust byproducts such as hydrocarbons, carbon monoxide, and nitrogen oxides to less hazardous gasses. However, TWCs use rare platinum group metals, which are both expensive and environmentally damaging to mine. Catalytically active aerogels such as cobalt-alumina, copper-alumina, and copper-silica have been shown to perform well as TWCs when subjected to a simulated automotive exhaust gas mixture. Global chemical kinetic models can be used to predict the performance of these catalysts and develop strategies for improving their behavior when exposed to a range of gas mixtures. This project extends a previously developed global kinetic model of cobalt-alumina aerogels that predicts the percent conversion of carbon monoxide and hydrocarbons with increasing temperature. The accuracy of the model is highly dependent on kinetic parameters that drive the reaction mechanisms such as reaction rate and activation energy. Two optimization methods were implemented to optimize each kinetic parameter. A Monte-Carlo genetic algorithm (MCGA) was initially used to optimize 12 kinetic parameters. Suitable fits were found using the MCGA; however it failed to converge to the same optimization minima and kinetic parameters during repeated runs. The model was simplified to focus on optimization of four key kinetic parameters, as these parameters were found to be the driving parameters behind the model. A lattice method algorithm was constructed in MATLAB and utilized least squares fitting to optimize the parameters. The algorithm converged to the same global minima for each trial and can be modified to model cobalt-alumina, copper-alumina, and copper-silica aerogels under different initial exhaust conditions.