Multiple Objective Optimisation (MOO) is a proven technique that can be employed by systems dynamicists as they seek to optimise parameters in simulation models. MOO employs genetic algorithms and Pareto-based ranking to find non-dominating sets of optimal solutions to problems that have more than one objective. The aim of this workshop is to: (1) Explain the multiple objective optimisation approach; (2) Show, though an interactive simulation model, how it can be applied to a popular system dynamics model (a two actor version of the beer game); (3) To explore with participants answers to a number of questions, including: (a) What kind of benefits can MOO bring over traditional optimisation approaches? (b) How do modellers decide on the appropriate payoff function? (c) How do decision makers approach the dilemmas of trading off two objectives? All participants will have access to a special purpose simulation application (Windows based) that will allow them to run simulations and optimisations on the two-agent beer game.