One of the impacts of rising demand for community health services is on long term capacity planning. Demand for community services arises directly from the community-mainly seniors- as well as from those discharged from the hospital. This thesis is focused on developing a simulation model based on patient flow in a set of community care facilities in order to help reduce the back log of patients remaining in acute care due to a lack of capacity in these facilities. Our model will provide the user with policy recommendations that address capacity allocation in different post-acute care alternatives over a multi-year time-horizon. In the model, patients differentiated by age and gender flow through the system with stochastic lengths of stay at each node (representing a facility type). We used historical data to classify patients. Proposed factors that influence the arrival and LOS parameters such as age and gender are tested on available data. We used Excel, Minitab and ARENA Input Analyzer to estimate the distribution of LOS, the arrival pattern and the age and gender distribution of new patients. We used Arena software for the simulation. The objective is to minimize patients waiting in the system subject to a constraint on the rate of expansion of facilities. Scenarios are informed by a previous queuing network model that provides the ideal capacity plan. The proposed method seeks to provide a means of determining the potential impact of various rates of expansion and changes in demand in order to more adequately plan future development.