Electrical energy demand has expanded exponentially due to population expansion and urbanization. Smart cities are using IoT and smart gadgets in homes to solve this problem. Demand response (DR) programs involve these devices in the power market. This study provides a cost-effective energy management system for IoT-based smart homes that reduces electricity prices, optimizes energy use, and manages peak to Average ratio (PAR). Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are optimized for price-based DR programs in this research. The report recommends utilizing GA and PSO to schedule smart home loads and regulate IoT device energy usage. The strategy reduces consumer energy expenditures and improves user comfort (UC) in smart homes, encouraging sustainability and cost-efficiency. This approach could improve global energy management by encouraging smart house residents to live sustainably.