Computer vision-based advanced driver assistance systems (ADAS) increase the safety of operations involving heavy machinery. ADAS systems using multiple cameras can be used for surround-view visualization of complex vehicles with blind spots. Such systems are also useful for autonomous vehicles. Multiple camera systems used to capture surrounding view of heavy machinery require complex design due to the complexity in size and shape of the vehicles. In this paper, we present a novel method for determining the optimal camera pose i.e. placement and orientation in three-dimensional space, given the shape of the vehicle, in order to maximize surrounding area coverage. The first method determines camera poses using a fixed pre-determined number of cameras, while the second method determines both camera poses and the number of cameras. The problem is modeled and solved using three different deterministic optimization algorithms: 1) single objective binary integer programming approach ; 2) single objective greedy algorithm ; and 3) a bi-objective binary integer programming approach. The methods are validated using a set of realistic 3-D vehicle models. Experimental validation has been conducted to compare the proposed methods with respect to coverage quality and computation time metrics. The experimental results have demonstrated that the proposed methods provide accurate solutions to the camera pose and the number of camera optimization.