The use of renewable solar and wind resources as distributed generation sources in distribution networks has been welcomed by network operators. In order to exploit the maximum benefits of using these distributed products, the location of installation and their capacity should be determined optimally in the distribution network. In this paper, in order to optimize the placement of solar panels and wind turbines in the distribution network with the aim of reducing losses and improving reliability based on Energy Not Supplied subscribers (ENS), a multi-objective evolutionary algorithm based on fuzzy decision method, called the Multi-Objective Hybrid Training Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) proposed that has a High optimization speed and not trapped at all in the optimal local. At first, the candidate buses are set for the installation of renewable resources using the Loss Sensitivity Factor (LSF). Then the proposed method is used to determine the location and optimal capacity of renewable resources through the candidate bases. Proposed issues have been implemented in a single-objective and multiobjective manner on a 33 bus IEEE radial distribution network. Also, in this paper, the effect of distributing renewable resources on the characteristics of the distribution network is evaluated. The results obtained from the proposed algorithm are compared with the results of other algorithms to demonstrate the superiority of the proposed method in reducing losses, improving reliability, and increasing the financial profit of the network. Simulation results show the better performance of the proposed method in comparison with Teaching-Learning Based Optimization (TLBO) and Grey Wolf Optimiser (GWO) methods and past studies to achieve optimal results. Also, the results show that distributing of the capacity and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.