OBJECTIVES:  Emergency Departments (EDs) are facing increasing volumes of patients with multiple comorbidities and are struggling to determine optimal staffing levels, secure resources and recruit/retain staff. Alternate strategies include matching the staff capacity to the patient demand. The goal of this study is to evaluate the improvement in quality of implementing a staffing optimization model. METHODS:  Staffing optimization was performed by the Emergency Department Clinical Engineering Learning Lab (ED-CELL) at a large midwest academic medical center to determine optimal levels of physicians, nurses, residents and APPs needed during various times of the days of the week. The engineering and emergency medicine practice worked together to operationalize the optimal staffing levels and made changes for a two-month period in year 2015. The coverage of ED staff (e.g. number of patients per MD) and time-based metrics (e.g. length of stay) were compared pre- and post- implementation for the same months from the previous year. RESULTS:  The coverage improved post-implementation, suggesting an improvement from a staffing perspective. However, length of stay did not show any relevant change. Data analysis revealed workflow-related changes that need to be addressed. CONCLUSIONS:  Optimization is a powerful technique that has provided sufficient gains in other industries. Healthcare has a few applications of optimization, yet fewer that have been translated into impact practice. Staffing is a major problem that affects ED performance and providing timely care to patients. The ED-CELL team developed an optimization model to match capacity ot demand, and implemented the results to improve staffing coverage. Length of stay did not exhibit much change, and key findings to improve workflow were identified.