Concerns the automation of the camera network design process in order to obtain accurate 3D measurements. We restrict ourselves to the problem where the camera positions are limited only by the incidence angle constraint and it is simplified to the case where the cameras remain at a fixed distance to the set of target points to be measured. The main question addressed is where to place the cameras in order to obtain the minimal 3D error. From this question several subproblems arise: how to develop a good criterion to judge the configuration; what conditions are needed for the system to work; which are the interrelated aspects involved in the development of the system; and how to optimize the placement of the camera. From these initial questions the choice of a criterion combined with an optimization process is the key concept. The approach can be divided into two main components. Firstly, we develop an uncertainty analysis based on error propagation. This allows us to express an error criterion to be minimized. Secondly, we present an evolutionary optimization method similar to genetic algorithm, which optimizes this criterion.