With the continuous growth of electricity demand during peak times, demand response (DR) programs have emerged as an effective tool for load management. However, incentive-based DR programs often face issues of instability and uncertainty. Traditional methods typically involved direct control of user equipment to reduce electricity use, but this approach is not feasible for households without controllable devices. As such, this paper discusses how to evaluate the existing instability and uncertainty of incentive-based DR by only providing load reduction incentives and signals to households and proposes an algorithm to find feasible plans. We propose an innovative 2-phase Monte Carlo Simulation Genetic Algorithm (2P-MCSGA) designed to maximize the likelihood of achieving load reduction within a specific budget. Our approach integrates Monte Carlo simulation with genetic algorithms, employing constraint relaxation and gene repair techniques to enhance solution feasibility and algorithm efficiency. This method ensures both the practicality and effectiveness of the DR plan, offering a novel solution to the challenges of incentive-based demand response.