The increasing energy demand and rising fossil fuel prices are accelerating the transition to renewable energy, supported by government initiatives due to their environmental and economic advantages. However, challenges such as limited capacity and stability constraints hinder the widespread adoption of distributed energy resources (DERs). Virtual Power Plants (VPPs) enhance market participation by aggregating DERs, while electric vehicles (EVs) contribute to environmental sustainability by reducing emissions. Additionally, integrating distribution static compensators (DSTATCOMs) within VPPs improves microgrid stability and reactive power support. This study proposes a two-stage optimization approach to enhance network resilience and VPP profitability in a radial distribution network (RDN). The first stage focuses on minimizing resilience-related costs and energy not supplied (ENS) during natural disasters, while the second stage optimizes VPP profit using a three-phase bidding strategy, which includes the day-ahead market, real-time market, and overall market. A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. To demonstrate the effectiveness of the proposed approach, IGWO-PSO is compared with other hybrid optimization algorithms. Validation on a modified IEEE 33-bus RDN confirms that the proposed model enhances VPP placement and sizing, leading to improved economic, operational, and resilience metrics. Furthermore, the model accounts for uncertainties in load demand, renewable generation, energy prices, and equipment availability, ensuring a robust and adaptable energy management strategy.