We present a simulation and multi-objective optimization framework for the integration of renewable generators and storage devices into an electrical distribution network. The framework searches for the optimal size and location of the distributed renewable generation units (DG). Uncertainties in renewable resources availability, components failure and repair events, loads and grid power supply are incorporated. A Monte Carlo simulation—optimal power flow (MCS-OPF) computational model is used to generate scenarios of the uncertain variables and evaluate the network electric performance. As a response to the need of monitoring and controlling the risk associated to the performance of the optimal DG-integrated network, we introduce the conditional value-at-risk (CVaR) measure into the framework. Multi-objective optimization (MOO) is done with respect to the minimization of the expectations of the global cost (C g ) and energy not supplied (ENS) combined with their respective CVaR values. The multi-objective optimization is performed by the fast non-dominated sorting genetic algorithm NSGA-II. For exemplification, the framework is applied to a distribution network derived from the IEEE 13 nodes test feeder. The results show that the MOO MCS-OPF framework is effective in finding an optimal DG-integrated network considering multiple sources of uncertainties. In addition, from the perspective of decision making, introducing the CVaR as a measure of risk enables the evaluation of trade-offs between optimal expected performances and risks.