DSM, or demand side management, is vital for the efficiency of smart grids. Since it reduces Electricity Costs (EC), and Home energy management systems, or HEMSs, are quickly emerging as essential instruments for maximizing family energy use. In this research, we propose a unique technique for appliance scheduling in residential settings that minimizes costs and ensures optimal delay: The Genetic Flower Pollination Algorithm (GFPA). The GFPA algorithm presented here is a hybrid method that integrates Flower Pollination Algorithm (FPA) and Genetic Algorithm (GA) components. By use of optimum scheduling patterns, our GFPA technique aims to simultaneously maximize User Comfort (UC) and minimize EC and PAR, having undergone rigorous testing at multiple sizes ranging from individual homes to bigger communities. Assuming stability in the types of appliances and patterns of power usage Simulation findings indicate that the GFPA technique improves the existing methods across households. To achieve optimal power scheduling, this research integrates demand response mechanisms and renewable energy sources into a new hybrid technique for smart grid management. It highlights how careful research and testing may improve grid dependability, sustainability, and cost-effectiveness. Through resolving current energy system issues and developing smart grid technologies, this research offers significant perspectives for developing a more dependable and effective energy future.