Dynamic voltage scaling (DVS) is a powerful technique for reducing dynamic power consumption in a computing system. However, as technology feature size continues to scale, leakage power is increasing and will limit power savings obtained by DVS alone. Previous system-level real-time scheduling approaches use DVS alone to optimize power consumption without considering leakage power. To overcome this limitation, we propose a new scheduling algorithm that combines DVS and adaptive body biasing (ABB) to simultaneously optimize both dynamic power consumption and leakage power consumption for real-time distributed embedded systems. First, we derive an analytical expression to determine the optimal supply voltage and body bias voltage under a given clock frequency. Based on this expression, we compute the optimal energy consumption at a given clock frequency and analyze the tradeoff between energy consumption and execution time for a set of tasks with precedence relationships and real-time constraints. We then propose a scheduling algorithm to reduce total power consumption under given real-time constraints. This algorithm also considers variations in power consumption of different tasks and characteristics of different voltage-scalable processing elements (PEs) to maximize power reduction. Experimental results show that the average power reduction of our technique with respect to DVS alone is 34.7%, while the average saving compared to no voltage scaling is 68.3% for the 0.07 μm technology.