We present a spatial Susceptible Exposed Infectious Recovered Vaccinated (SEIRV) agent-based model designed to evaluate vaccination strategies under different epidemic and population conditions. The model represents individual agents on a grid, each following probabilistic infection, recovery, and vaccination dynamics influenced by behavioral compliance and vaccine efficacy. Five allocation strategies - hotspot, targeted, risk-based, ring, and weighted-random - are compared across varying transmissibility factors and population sizes. Simulation results show that the hotspot strategy consistently achieves the shortest epidemic duration, lowest infection peak, and smallest attack rate. Targeted vaccination performs comparably in small or mild outbreaks but loses efficiency in larger or faster epidemics. Risk and ring strategies yield intermediate outcomes, while weighted-random allocation performs worst in all conditions. These findings highlight the advantages of spatially concentrated vaccination and show how epidemic controllability depends on transmissibility, population scale, and behavioral diversity. The model provides a reproducible and extensible framework for analyzing vaccination policies within spatially structured agent-based systems.