Particle Swarm and the Genetic Algorithm were coupled to optimize multiple performance metrics for the combustion of neat biodiesel in a turbocharged, four cylinder, John Deere engine operating under constant partial load. The enhanced algorithm was used with five inputs including EGR, injection pressure, and the timing/distribution of fuel between a pilot and main injection. A merit function was defined and used to minimize five output parameters including CO, NOx, PM, HC and fuel consumption simultaneously. The combination of PSO and GA yielded convergence to a Pareto regime without the need for excessive engine runs. Results along the Pareto front illustrate the tradeoff between NOx and particulate matter seen in the literature. By using an injection pressure of 173 MPa, pilot injection and the unique properties of neat biodiesel, the application of almost 50% EGR could be applied to reduce NOx emissions to 0.72 g/kW-h, while keeping emissions of HC, CO and PM below the Tier 4 limits. These results were found with a late pilot injection at -1.780 ATDC and a main injection at 3.130 ATDC. The best ratio of pilot fuel to main fuel for this timing was 45%. Retarding main and pilot timing toward TDC reduces NOx emissions, and the proximity between injections is shown in the heat release and emissions data to be beneficial to the reduction of HC, CO and PM emissions.