Distributed multi-input multi-output (MIMO) system is a promising architecture to provide reliable communications over spatially separated relaying nodes. In this paper, we will investigate the optimum resource allocation techniques in distributed MIMO systems, employing differential (de)modulation and various relaying protocols. Instead of limiting to energy optimization, we solve this problem via a two-dimensional energy and location optimization. The benefits of our resource optimization approaches are illustrated through extensive analysis and simulations. Both the comparisons between different optimization techniques and systems with different protocols are included.Based on our previous work, we provide an upper bound of the symbol error rate (SER) for decode-and-forward (DF) systems and an approximated SER for amplify-and-forward (AF) systems. We proved that the system SER is a convex function in both the transmit energy and the relay location, and then carried out the two-dimensional energy and location optimization through numerical search. As we know, system energy is a limited resource, especially for mobile terminals, and the coverage area is a critical factor to judge the system performance. Therefore, we evaluate the benefits of our optimization techniques in terms of the system energy saving and the coverage distance extension.The simulations reveal several interesting results. For both the DF and the AF protocols, the optimized systems always outperform the unoptimized systems with either less energy consumption or longer transmission range. It is also noticed that the benefits of both energy and location optimizations vary a lot for different protocols, and with different system configurations. Uniform energy allocation and midpoint relay location are normally chosen as an initial system setup. For such a configuration with the DF protocol, location optimization is more critical than energy optimization, and the unoptimized system receives prominent benefits from both optimizations, and tremendous system resources savings. For the AF protocol, however, the location and energy optimizations are equally important for the unoptimized system. It turns out that the uniform energy allocation and the midpoint relay location result in fairly good system performance, since it is reasonably close to the global optimum.For other initial system setups, the optimization benefits are also distinct in the AF and the DF systems. In DF systems, more optimization benefits can be achieved when the relays are either close to the destination or have more transmit energy allocated to the relay(s). On the contrary, in the AF systems, remarkable optimization benefits will be achieved when the relays are far from the midpoint, or when the relays are only able to transmit at low energy levels.