Genomic data is rapidly increasing for P. vivax isolates around the world and is increasingly used to infer the origin, spread, and adaptation of parasites. Yet, patterns of genetic variation in malaria parasites remain difficult to interpret, and few formal statistical inference frameworks exist. In particular, multiple epidemiological factors and the parasite’s complex lifecycle shape observed genetic variation and limit the applicability of common population-genetic models. Leveraging recent developments in genomic forward simulators, we developed a whole-genome simulation of P. vivax that incorporates realistic features of parasite life history and epidemiology in SLiM, including recombination and multiple bottlenecks. We include a human layer with asexual reproduction and mosquito layer with sexual reproduction, linked through a stochastic Ross MacDonald model. Using the model, we study the expected genetic diversity of parasite populations as a function of model parameters, such as transmission rates and population sizes at multiple time points. For example, we find that the size of a transmission bottleneck that seeds human infection has a larger effect on measures of variation (such as pairwise diversity or the site frequency spectrum) than the carrying capacity of parasites within a human host does. This result is consistent with population genetic theory in which smaller population sizes over time have disproportionately large impacts on the effective population size and diversity. The model can be used to test the impact of interventions on parasite genetic diversity through simple adjustments to parameters, providing insight into mechanisms or progress towards controlling and eradicating malaria. This work can also form the basis for simulation-based inference of parasite population history or adaptation. More generally, understanding of the neutral genetic diversity of P. vivax informs our interpretation of infection rates, connectivity between outbreaks, and adaptations which allow malaria to evade interventions such as medications.