The document presents a nature-inspired Big Bang-Big Crunch (BB-BC) algorithm for minimizing power consumption in embedded systems. The BB-BC algorithm mimics the big bang theory of evolution with an initial random solution generation phase followed by an iterative convergence to the center of mass. The algorithm is shown to reduce energy consumption in system memory from 76-98% compared to Tabu Search, with better accuracy and convergence. It provides an effective method for power management in memory-intensive embedded systems.