The translocation of individuals into genetically divergent populations – “genetic mixing” or “genetic rescue” – is useful for recovering the viability of bottlenecked populations. However, limited understanding of how various demographic and genetic characteristics might influence the outcome of genetic mixing has hindered its uptake in conservation practice. In particular, managers are often faced with the challenging decision of where – if at all – to source individuals for genetic rescue. Using a genetically explicit simulation framework based on the genomes of four mammal species, I investigated how different demographic characteristics (e.g., population sizes; level of divergence; number of individuals translocated) impact the efficacy of genetic mixing. I then assessed the relative importance of these variables on mixing outcomes using generalised linear models. Preliminary results indicate that population sizes have the greatest impact on genetic mixing efficacy – with larger and more diverse source populations providing the strongest benefit. Contrastingly, the number of individuals translocated had a much smaller impact, indicating that positive outcomes from genetic mixing can occur even when few individuals are translocated. From these findings, I developed a novel Potential Source Index which quantifies the relative suitability of potential source populations across a dataset. This simple index will provide managers with an interactive and accessible decision-making framework for choosing source populations in real genetic mixing programs.