The rapid globalization of the world economy has led to the development of ample and quickly growing (aerial, maritime, terrestrial) networks for merchandise distribution in containers [Wang et al., 2008]. The transport costs afforded by the specialized companies operating in this sector are directly related to appropriate loading and efficient use of space [Xue and Lai, 1997a]. The efficient loading of a set of containers can be done technically by solving the Container Loading Problem (CLP). CLPs are NP-Hard problems that basically consist in placing a series of rectangular boxes inside a rectangular container of known dimensions, seeking to optimize volume utilization [Pisinger, 2002], and taking into consideration the basic constraints enounced by Wascher et al. (2007): (i) all the boxes must be totally accommodated inside the container, and (ii) boxes should not overlap. Notwithstanding, the solving of actual container loading problems can be limited or rendered inappropriate if only these two constraints are considered [Bischoff and Ratcliff, 1995; Bortfeldt and Gehring, 2001; Eley 2002]. In this sense, Bischoff and Ratcliff (1995) enounced a series of practical restrictions that are applicable to real situations: orientation and handling constraints, load stability, grouping, separation and load bearing strength of items within a container, multi-drop situations, complete shipment of certain item groups, shipment priorities, complexity of the loading arrangement, container weight limit and weight distribution within the container. According to the literature on the topic, these considerations have not been included in many of the existing approaches to the CLP problem. Some of these criteria are difficult to quantify [ibidem] due to their qualitative nature. The traditional optimization approaches, which cardinalize qualitative aspects, tend to cause loss of important criterion information. For this reason, more natural treatments such as those resulting from ordinal approaches are advisable [Garcia et al., 2009]. The CLP has a natural correspondence with the integral optimization concept, which includes qualitative and quantitative criteria within an optimization problem [ibidem]. The CLP solving approach treated here not only considers the fundamental quantitative criteria stated by Wascher et al. (2007), but two other important ones contributed by Bischoff and Ratcliff (1995) as well: i) not exceeding the container's weight transportation limit, and ii) once the container has been loaded, its center of gravity (COG) should be close to the geometrical center of its base (weight distribution within a container). In turn, the