The need to manage project risk, through the use of decision analysis tools and other approaches will only increase in the future. Several statistical methods are applied to simulate, in a project risk environment, a simple multiobjective optimization problem. Through the use of the NSGA-II genetic algorithm, coded in Python, and the employment of PROMETHEE to rank the alternatives generated, a method for determining where along a Pareto frontier to focus effort upon to produce the maximum amount of risk mitigation will be introduced. The combined use of these decision analysis tools and the presentation, in context to project risk within project networks, is innovative in the area of Multi-Criteria Decision Making and Project Risk Management.