Optimization via Simulation applied to biological systems is a powerful tool for conducting large-scale, automated in silico experiments aimed at improving real-world processes. This field presents significant challenges, as the complexity of the simulated biological systems results in complex, high-dimensional, black-box functions that are difficult to optimize algorithmically, alongside the substantial computational cost of repeated simulations. A preliminary phase of this work addresses the algorithm decision problem for non-convex function optimization, proposing a model that estimates the performance of different optimization meta-heuristics on arbitrary functions using statistical Fitness Landscape Analysis measures. The study then applies a multi-scale model of tumor growth and cell resistance to treatments to optimize the delivery strategy of tumor necrosis factors (TNF).