This paper presents and demonstrates an automated generic approach to improving the accuracy and stability of iterative pose estimation in computer vision applications. The class of problem involves the use of calibrated CCD camera video imagery to compute the pose of a slowly moving object based on an arrangement of visual targets on the surface of the object. The basis of stereo-vision algorithms is to minimize a re-projection error cost function. The proposed method estimates the optimal target locations within the area of interest. The optimal target configuration delivers the minimal condition number of the linear system associated with the iterative algorithm. The method is demonstrated for the case when targets are located within a 3D domain. Two pose estimation algorithms are compared: single camera and two-camera algorithms. A better accuracy in pose estimation can be achieved with a single camera algorithm with optimized target locations. Also, this method can be applied to perform optimization of target locations attached to a 2D surface.