We will describe a cell trafficking network model for metastatic breast cancer based on evolutionary dynamics in game theory. Trafficking of individual cells via the circulation between 31 anatomical sites are based on probabilities obtained from the Markov transition matrix developed from the longitudinal data set compiled by the Memorial Sloan-Kettering Cancer Center. The data contains 449 newly diagnosed breast cancer patients between 1975 and 2009 that all eventually developed metastatic disease. Cell population dynamics within each anatomical site are dictated by the replicator equation. Competition among types of cells (healthy, preoncogenic, oncogenic) is determined by a payoff matrix with prescribed cell fitness entries, as well their relative proportions via the replicator equation. Individual cells within each anatomical site can mutate into a cancerous (preoncogenic or oncogenic) cell type, give birth to new daughter cells, undergo apoptosis, intravasate into the circulation state, or extravasate into a secondary anatomical site. We will describe a  set of Monte Carlo computational simulations focusing on sites previously identified as spreaders or sponges by the Newton-Mason model. We will describe the model in some detail, then show how it can be used to test various hypothetical therapeutic strategies associated with metastatic breast cancer.