With the acceleration of energy reform, photovoltaic, energy storage, electric vehicles, and other new loads in low-voltage distribution networks have been rapidly developed. However, the distribution network with distributed power supply has some problems, such as imprecise power flow modeling and difficult coordination between various energy sources and loads, which bring challenges to the online optimization of the distribution network load curve. In this study, a multi-time scale online load optimal operation scheme of the distribution network is proposed by using the Bayesian online learning method. This scheme transforms the online power optimization of the distribution network into a Markov decision process. The output time of different energy sources is different, and the load with different user load characteristics is optimized. The scheme can track the state of the distribution network in real time and make the optimization scheme of multi-energy output online. Finally, an example is given to verify the effectiveness of the proposed method, which has theoretical significance for promoting the diversified development of low-voltage user-side load.