In recent decades, the increase in widespread outages caused by unexpected events such as floods and storms has doubled the importance of resilience studies in power distribution networks. Therefore, by timely identifying the location of the incident and isolating it, as well as designing MGs that are capable of providing their maximum load by planning the energy resources available under the incident conditions, the costs related to outages can be significantly reduced and the network resilience can be improved. In this paper, an optimization problem with three objective functions is introduced, the first term of which aims to reduce the cost of turning on/off the DGs available in each MG and also to reduce the costs related to battery charging/discharging in MGs. In the second term, to increase the self-sufficiency of MGs, an objective function has been introduced so that MGs can provide their required power as much as the energy buying and selling market between MGs and the upstream network allows, and increase their profits by charging and discharging batteries and turning on and off their DGs at different times of the day and night, as well as based on the proposed price of the upstream network. Finally, in the third term, the proposed objective function will seek to reduce the losses in the MGs under study. To solve the proposed objective function, a combined optimization method based on particle and genetic algorithms has been used. The results are implemented on a 33-bus test network and show that the proposed method outperforms conventional optimization methods. Also, the proposed optimal scheduling in MGs reduced losses and increased their profits.