Femoroplasty is the process of injecting cement (cement augmentation) into the proximal femur to prevent osteoporotic hip fractures. Femoroplasty increases the strength and energy to failure of the femur and can be performed in a minimally-invasively manner with lower hospitalization costs and reduced recovery. Our hypothesis was that efficient cement augmentation strategies can be identified via computational optimization. Therefore, using patient-specific planning we can minimize cement volume while increasing bone strength and reducing the risk of fracture. We proposed an in-silico methodology that was validated with in vitro experiments. A discrete particle model for cement infiltration was used to determine the optimum volume and filling pattern of the cement such that the best outcome was achieved. Several artificial bones were scanned before and after cement augmentation to applied previous in silico methodology. Then those femurs were mechanically tested (non-augmented and augmented). Therefore, in silico methodology was validated. Cement augmentation significantly increased the yield load. Predicted yield loads correlated well with the experiments. Results suggest that patient-specific planning of femoroplasty reduces the risk of hip fracture while minimizing the amount of cement required.