This research introduces a relatively new evolutionary algorithm in computer science; Grammatical Evolution (GE), to the field of supply chain dynamics and bullwhip mitigation. As a proof of concept several experiments are conducted to derive optimal ordering policies for agents in a multi-tier supply chain. These results are compared with existing research using Genetic Algorithms (GA) to derive optimal ordering policies using similar simulations. This paper shows that GE can consistently discover the optimal ordering policies similar to the GA approach, and that in several experiments GE outperforms GA.