Abstract: The participation of giant hydropower stations in the power market in southwest China involves the decomposition of multi-dimensional power curve with multiple provinces, varieties and time scales, which brings great challenges to the market operation of hydropower. This article relies on the actual project of Xiluodu Right Bank Power Station, a multi-objective coordination method of monthly electric quantity curve decomposition for giant hydropower stations is proposed considering the complex needs of network peak adjustment and enterprise benefit. The peak shaving goal of multi-grid is constructed with the minimum residual load mean square error, and the generation efficiency goal is constructed with the multi-variety differential electricity price and peak-flat-valley segmented market electricity price. The ideal point method is introduced to achieve the goal classification, and then the multi-objective optimization is transformed into a series of single-objective problems with differential constraint boundaries. The peak shaving goal of multi-grid is constructed with the minimum residual load mean square error, and the generation efficiency goal is constructed with the multi-variety differential electricity price and peak-flat-valley segmented market electricity price. The piecewise linear technique is used to deal with the nonlinear hydraulic constraints, the triangulation technique is used to deal with the power station output characteristic curve, and the mixed integer linear programming (MILP) method is used to solve the model efficiently. The actual data of the monthly electricity transaction declaration of right bank of Xiluodu Hydropower Station is used for verification and analysis. The results show that the model in this paper can obtain a reasonable monthly electricity generation curve process of daily, hour, province, and category. Through two examples to analyze the impact of different incoming water and planned electricity on peak shaving and revenue, it can be found that the impact of flood season is greater than that of dry season, and the proportion of planned electricity is an important factor in coordinating peak shaving and power generation revenue.