The concept of metacognition is a framework that allows an artificial intelligence system to think about what it thinks. This enables it to perform various tasks such as memory representation, retrieval, and mining. Complex constructs in metacognition are important factors that affect the development of artificial intelligence systems that can learn and use memory. Various metacognition and memory elements are used in designing and developing AI systems designed to perform missions. One of these is the ability to make a judgment about the amount of information it has available to complete a task. This is done through the Emergent Behavior concept, which allows a system to maintain its attention while performing the analytical process. The use of the genetic algorithm in engineering is regarded as an adaptive technique that can be used to solve complicated problems. This meta-heuristic approach is commonly used in hybrid computation. It utilizes mutation operators, selection, and crossover techniques to manage the system strategy. The genetic algorithm is derived from the concepts of natural selection and genetics. It can be used to improve the performance of a coverage framework by utilizing a random search method. These techniques are often used in statistical analysis to find a suitable solution to a given problem or prevent an error-prone strategy from being implemented in a search. They were developed from genetics or natural selection principles. The project aims to create a meta-cognitive phase that cognitive architectures may leverage to execute essential tasks using the Evolutionary Algorithm like Genetic Algorithm in MIDCA architecture. This is one of the first attempts to integrate a Genetic Algorithm into the metacognitive phase and reason out based on the energy consumption on a real-time residential dataset.