The time it takes to download a file in a peer-to-peer (P2P) overlay network is dependent on several factors. These factors include the quality of the network between peers (e.g. packet loss, latency, and link failures), distance, peer selection technique, and packet loss due to Internet Service Providers (ISPs) engaging in traffic shaping. Recent research shows that P2P download time is adversely impacted by the presence of distant peers, particularly when traffic goes across an ISP that could be engaging in P2P traffic throttling activities. It has also been observed that additional delays are introduced when distant candidate nodes for exchanging data are included during the formation of a P2P network overlay. Researchers have shifted their attention to the mechanism for peer selection. They started questioning the random technique because it ignores the location of nodes in the topology of the underlying physical network. Therefore, selecting nodes for interaction in a distributed system based on their position in the network continues to be an active area of research. The goal of this work was to reduce the cumulative file download time and variance for the majority of participating peers in a P2P network by using a peer selection mechanism that favors nearby nodes. In this proposed proximity strategy, the Internet address space is separated by IP blocks that belong to different Autonomous Systems (AS). IP blocks are further broken up into subsets named zones. Each zone is given a landmark (a.k.a. beacon), for example routers or DNS servers, with a known geographical location. At the time peers joined the network, peers were grouped into zones based on their geographical distance to the selected beacons. Peers that end up in the same zone were put at the top of the list of available nodes for interactions during the formation of the overlay. Experiments were conducted to compare the proposed proximity based peer selection strategy to the random peer selection strategy. The results indicate that the proximity technique outperforms the random approach for peer selection in a network with low packet loss and latency and also in a more realistic network subject to packet loss, traffic shaping and long distances. However, this improved performance came at the cost of additional memory (230 megabytes) and to a lesser extent some additional CPU cycles to run the additional subroutines needed to group peers into zones. The framework and algorithms developed for this work made it possible to implement a fully functioning prototype that implements the proximity strategy. This prototype enabled high fidelity testing with a real client implementation in real networks including the Internet. This made it possible to test without having to rely exclusively on event-driven simulations to prove the hypothesis.