The scalability of routing architectures for large networks is one of the biggest challenges that the Internet faces today. Greedy routing, in which each node is assigned a locator used as a distance metric, recently received increased attention from researchers and is considered as a potential solution for scalable routing. In this paper, we propose LMD - a Local Minimum Driven method to compute the topology-based locator. As opposed to previous work, our algorithm employs a quasigreedy and self-organized embedding method, which outperforms similar decentralized algorithms by up to 20% in success rate. To eliminate the negative effect of the “quasi” greedy property - transfer routes longer than the shortest routes, we introduce a two-stage routing strategy, which combines the greedy routing with source routing. The greedy routing path discovered and compressed in the first stage is then used by the following source-routing stage. Through extensive evaluations, based on synthetic topologies as well as on a snapshot of the real Internet AS topology, we show that LMD guarantees 100% delivery rate on large networks with a very low stretch.