The international migration network, comprising the movements of people between countries, is one of the most important global systems of interaction, which can reflect the complex international relations of economics, cultures, and politics and has huge impacts on global sustainability. However, the conventional gravity model cannot model its complicated interactions accurately. In this article, we propose a novel reverse gravity model using genetic algorithm to reconstruct the complicated interaction patterns with high accuracy. To verify the feasibility of our method, it was applied to a series of international migration networks. We found that the derived node attractions were highly correlated with socioeconomic factors and network metrics, and the calculated node positions outperformed the geometric centers from the perspective of human migration that related to economy and demography. Our approach could be a preferred choice to investigate the spatial–temporal interactive patterns in geographical space, facilitating comprehension of the mechanisms underlying their generation and evolution.