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The high penetration of distributed energy resources (DERs) introduces several challenges to the power network. They may cause a high level of voltage variation, sudden over/under-power generation, high power losses, and negatively impacteddistribution assets. Thus, there is a vital need for volt/var optimization (VVO) schemes that integrate utility-owned assets with inverter-interfaced resources to overcome these challenges. This thesis addresses the above-mentioned challenges by proposing a comprehensive two-timescale multi-objective VVO algorithm. The slow timescaleutilizes utility-owned assets to minimize system losses and maximize asset lifetime ina three-step methodology. This stage incorporates the utility operator's direct input for setting the utility-owned assets. At the faster scale, the algorithm optimizes the reactive power of DERs to minimize voltage variations and system losses. The proposed VVO is solved using conventional optimization and reinforcement learning algorithms. The IEEE 33-bus system is modified and used to demonstrate the effectiveness of the proposed algorithms