This paper presents a fundamental study for optimization of emergency restoration plans after a disaster and stock management plans as disaster prevention/mitigation measures for sewer networks based on quantitative evaluation of the resilience. The sewer network was modeled as a directed tree having a root at the most downstream manhole, considering the cumulative flow at the root as a function of the network. The resilience can be quantitively evaluated by reproducing post-disaster functional loss and recovery processes from a disaster. An optimization of emergency restoration plans identified the most effective restoration sequence for eliminating blockages in sewer pipes using the mixed integer linear programming. Meanwhile, an optimization of stock management plans determined the multi-year phased retrofit priority of sewer pipes to enhance the resilience of the sewer network using the genetic algorithm. This study proposed a method to establish evidence-based and effective disaster countermeasures for sewer networks upon understanding functional transition from a disaster.