The approach is to include forwarding state scalability as one of the optimal objective when constructing new multicast trees. This multi-objective optimization approach can be applied to many existing multicast state reduction methods. In this paper, the approach is illustrated by applying it to aggregated multicast (AM) and dynamic tunnel multicast (DTM). Both AM and DTM routing problems are formulated as multi- objective optimization problems, and both heuristic and genetic algorithms are proposed for solving them. Based on the experimental results, the approach can further improve the forwarding state scalability of both approaches by reducing the number of aggregated trees required by the AM method, and by increasing the number of non-branching nodes for the DTM method.