This paper describes a two-phase simulation optimisation algorithm that integrates the genetic algorithm and response surface-based linear search algorithm for developing an optimal cyclic plan in a multi-echelon environment during the maturity phase of the life cycle of a product. The problem involves a search in a high dimensional space with different ranges for decision variables scales, multiple objective functions and problem specific constraints. The paper provides an illustrative example of the two-phase simulation optimisation algorithm applied to a generic supply chain network.