This article addresses the placement and sizing of virtual power plants (VPPs) in distribution networks to enhance grid resilience against severe weather events such as earthquakes and floods. The problem is formulated as a two-objective optimization, aiming to minimize the expected energy not-supplied during extreme weather conditions and the annual planning cost of VPPs. The expected energy not-supplied serves as a resilience index. Constraints include VPP planning formulas, network operation limitations, and AC power flow equations. Stochastic programming accounts for uncertainties in demand, renewable power, energy prices, and hardware supply. A Pareto optimization strategy based on the ε-constraint method is used to achieve a single-objective formulation. A hybrid meta-heuristic algorithm combining the Crow search algorithm (CSA) and sine cosine algorithm (SCA) is employed to find an optimal solution with minimal dispersion. The proposed method evaluates how VPPs affect network resilience and scales them accordingly. Numerical results on a 69-bus distribution network demonstrate the effectiveness of the optimal placement and sizing of VPPs in improving economic status, utilization, and resilience.This article addresses the placement and sizing of virtual power plants (VPPs) in distribution networks to enhance grid resilience against severe weather events such as earthquakes and floods. The problem is formulated as a two-objective optimization, aiming to minimize the expected energy not-supplied during extreme weather conditions and the annual planning cost of VPPs. The expected energy not-supplied serves as a resilience index. Constraints include VPP planning formulas, network operation limitations, and AC power flow equations. Stochastic programming accounts for uncertainties in demand, renewable power, energy prices, and hardware supply. A Pareto optimization strategy based on the ε-constraint method is used to achieve a single-objective formulation. A hybrid meta-heuristic algorithm combining the Crow search algorithm (CSA) and sine cosine algorithm (SCA) is employed to find an optimal solution with minimal dispersion. The proposed method evaluates how VPPs affect network resilience and scales them accordingly. Numerical results on a 69-bus distribution network demonstrate the effectiveness of the optimal placement and sizing of VPPs in improving economic status, utilization, and resilience.