A major goal of smart grid technology (e.g., smart meters) is to provide consumers with demand response signals such as electricity tariff and CO 2 footprint so that the consumers can consciously control their electricity consumption patterns. These demand response signals provide incentives for the consumers to help reduce peak energy demand by load balancing, as this is particularly relevant in a situation with high level of renewable energy penetration. However, the volume of information can be overwhelming for the consumers. Further, in some situation minimization of electricity bill and CO2 emission can be conflicting goals and a trade-off analysis is required. To enable the consumers to participate in smart grid effort this paper proposes a decision aiding framework for optimal household appliances scheduling and trade-off analysis through Pareto frontier exploration. To compute the optimal schedules associated with Pareto optimal points, linear optimization problems with SOS2 (special ordered set of type 2) constraints are solved using CPLEX, in the case where the demand response signals are assumed to be piecewise constant. For arbitrary demand response signals, a corresponding dynamic programming solution is proposed. A numerical study demonstrates that in a realistic test case the Pareto frontier analysis can provide valuable information leading to schedules with drastically different electricity and CO2 emission patterns. In addition, the case study verifies that the Pareto frontier can be computed in real-time in a realistic residential computing environment.