The combination of these objectives-PES shape and RMSE alignment, gradients difference's norm minimization, and charge adjustment-constitutes a MOOP. Our approach employs an evolutionary algorithm to navigate the complex parameter space and achieve an optimal balance across these objectives. This ensures that the re-parametrized GFN2-xTB Hamiltonian is not only accurate in terms of energetics but also reliable in predicting geometries and electrostatic properties, making it a powerful tool for modeling enzymatic reactions.