In deregulated electricity markets, generation companies with the aim of maximum revenue need to provide trading strategies to the electricity trading market, which contributed to a self-scheduling model. When considering the uncertainty of price, the trading strategies are required to maximize the revenue as well as minimizing the risks brought by uncertainties. In this paper, a multi-objective robust mean-variance model was proposed to solve the above problem and the Pareto frontier of the multi-objective optimization was obtained. Moreover, the proposed robust mean-variance model could be equivalently transformed into a non-robust mean-variance model which was casted as a second-order cone programming (SOCP) optimization. The price of robustness to benefits, risks, and the Pareto frontier were analyzed. Finally, the robust mean-variance model based self-scheduling model optimization and its budget of robustness were tested on a 30-bus system. The simulation results demonstrate the effectiveness of the proposed method and analysis.