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Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2–A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2–B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids’ peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing carbon emissions, CPP-PDC reduces the maximum demand for electricity from the grid and the overall demand above the predetermined level. The maximum power demand from the grid is minimized for points A5 and B5, reducing up to 22% compared to A2. The proposed method minimizes both energy costs as well as CO2 emissions. A Pareto curve illustrates the trade-off between cost and CO2 emissions.