In today's embedded systems, engineers are trying to get as much performance out of designs while minimizing the energy consumed in order to maximize battery life. Furthermore, embedded systems and their computational sub-systems are becoming more heterogeneous, containing compute resources such as general-purpose processors, graphics processing units, and FPGAs. Because of this heterogeneity, there is a rich area for optimization, especially when considering the mapping of a dynamic, real-time application to these heterogeneous resources. One approach involves maximizing the performance of a task on a given architecture with a given energy constraint. However, this approach will not minimize power and energy consumption. Therefore, in this paper, we propose new dynamic runtime optimizations that can schedule dynamic tasks to a heterogeneous system while minimizing energy consumption and deadlines missed. Through experimentation, we found improvements in energy efficiency of up to 390× relative to a baseline greedy scheduler.