Reasoning with continuously changing numeric quantities is vital to applying planners in many real-world scenarios. Several planners capable of doing this have been developed recently. Scalability remains a challenge for such planners, especially those that reason with non-linear continuous change. In this paper, we present a novel approach to reasoning with non-linear domains. Bounding the problem using linear over and under-estimators, allows us to use scalable planners that handle linear change to find plans for non-linear domains. We compare the performance of our approach to existing planners on several domains and demonstrate that our planner can achieve state-of-the-art performance in non-linear planning.