Large scale outages on real-world critical infrastructures (CIs), although infrequent, are increasingly disastrous to our society. In this paper, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multi-objective optimization is carried out by a non-dominated sorting binary differential evolution (NSBDE) algorithm. For each generators-distributors connection pattern considered in the NSBDE search, a computationally-cheap, topological model of failure cascading in a complex network (named, the Motter-Lai (ML) model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400kV French power transmission network case study show that the proposed method allows to identify optimal patterns of generators-distributors connection which improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with embedded ML topological model, a more realistic but also more computationally-expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL-Pserc-Alaska (OPA) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability and low computational cost.