This study presents a network architecture for an intelligent residential energy-saving system built on Internet of Things (IoT) technology. The sensing layer is implemented using ZigBee, while remote supervision and control are enabled through Internet connectivity and GPRS. The proposed system integrates multiple functions, including home security, smart regulation of lighting and indoor temperature, household-level heat metering, and electricity-use monitoring. To improve energy performance, an electricity-consumption optimization model is developed using an enhanced genetic algorithm. Field experiments and data analysis confirm the system’s effectiveness in regulating indoor thermal conditions and optimizing energy use. In the cooling season, smart housing most frequently maintains an indoor temperature of 26°C (26.5% of observations), whereas conventional housing most often records 28°C (17.5%). Smart housing also exhibits a slightly narrower indoor relative-humidity range. During the heating season, thermal comfort in smart housing remains notably better than in conventional residences, and the IoT system significantly shortens periods when humidity exceeds acceptable limits. Economic analysis further indicates diminishing returns: as the targeted energy-saving proportion rises, the incremental economic benefit of reducing consumption declines—moving from about 3% at 4,629.52 yuan/m² to about 13% at 2,023.47 yuan/m²—suggesting that higher energy-saving targets may yield smaller marginal financial gains.