The orthopedic industry is still searching for an efficient way to replace bone lost due to surgical procedures such as arthroplasty and limb-sparing surgery. Additive manufacturing (AM) presents an opportunity to manufacture affordable patient-specific implants. Although previous works have investigated the viability of AM in bone implants, producing defect-free implants has yet to be mastered. Optimization of the implant design to maximize osseointegration (bone ingrowth) has not been appropriately addressed. This thesis proposes a novel approach for modeling the microstructure evolution and osseointegration processes for orthopedic AM implants. Agent-Based Modeling (ABM) is a cellular automata based computing technique that uses simple rules derived from experimental studies to simulate evolutionary phenomena. In this thesis, grain growth during sintering of an AM TiO2 sample test cube and osseointegration in this cube have been modeled using ABM. The results are validated by comparison to experimental studies and several conclusions are drawn.