The cooling systems of rapidly growing Data Centers (DCs) consume a considerable amount of energy, which is one of the main concems in designing and operating DCs. The main source of thermal inefficiency in a typical air-cooled DC is hot air recirculation from outlets of servers into their inlets, causing hot spots and leading to performance reduction of the cooling system. In this study, a thermally aware workload spreading method is proposed for reducing the hot spots while the total allocated server workload is increased. The core of this methodology lies in developing an appropriate thermal DC model for the optimization process. Given the fact that utilizing a high-fidelity thermal model of a DC is highly time consuming in the optimization process, a three dimensional reduced order model of a real DC is developed in this study. This model, whose boundary conditions are determined based on measurement data of an operational DC, is developed based on the potential flow theory updated with the Rankine vortex to account for buoyancy and air recirculation effects inside the DC. Before evaluating the proposed method, this model is verified with a computational fluid dynamic (CFD) model simulated with the same boundary conditions. The efficient load spreading method is achieved by applying a multi-objective particle swarm optimization (MOPSO) algorithm whose objectives are to minimize the hot spot occurrences and to maximize the total workload allocated to servers. In this case study, by applying the proposed method, the Coefficient of Performance (COP) of the cooling system is increased by 17%, and the total allocated workload is increased by 10%. These results demonstrate the effectiveness of the proposed method for energy efficiency enhancement of DCs.