For mobile embedded systems, the energy consumption is a limiting factor because of today’s battery capacities. Besides the processor, memory accesses consume a high amount of energy. The use of additional less power hungry memories like caches or scratchpads is thus common. This paper presents a combined approach for energy consumption minimization consisting of two complementary and phase-coupled optimizations, viz. data partitioning and loop nest splitting. In a first step, data partitioning partitions large arrays found in typical embedded software into smaller ones which are placed onto an on-chip scratchpad memory. Although being effective w. r. t. energy dissipation, this optimization adds overhead to the code since the correct part of a partitioned array has to be selected at runtime. Therefore, the control flow is optimized as a second step in our framework. In this phase, loop nests containing if-statements are split using genetic algorithms leading to minimized if-statem...