Projects are considered interrelated when their benefits or costs depend on which other projects are implemented. Selection and scheduling of interrelated projects is a challenging optimization problem which has applications in various fields including economics, operations research, business, management and transportation. The goal is to determine which projects should be selected and when they should be funded in order to minimize the total system cost over a planning horizon subject to a budget constraint. The budget is supplied by both external and internal sources from fuel tax revenues. This study then applies three meta-heuristic algorithms including a Genetic Algorithm (GA), Simulated Annealing (SA) and, Tabu Search (TS) in seeking efficient and consistent solutions to the selection and scheduling problem. These approaches are applied to a special case of link capacity expansion projects to showcase their functionality and compare their performance in terms of solution quality, computation time and consistency.