A new procedure is presented for point cloud optimization based on the reduction of the Hausdorff distance (error metric) between a point cloud and the 3D object it represents. This approach is crucial for accurately representing a 3D object while using a minimal number of points. This is achieved by utilizing two point clouds of the same 3D object but with different point densities: a high-density point cloud (original) and a low-density point cloud (simplified). The high-density cloud guides the reordering of points in the simplified cloud, ensuring alignment with the original positions. The methodology of this work is as follows. Each simplified point cloud is scaled to match the range of the original cloud. Both clouds are then aligned in the same spatial domain to establish neighborhoods of points in the original cloud that correspond to one or more points in the simplified cloud. Finally, the points in the simplified cloud are reordered based on their respective neighborhoods in the original cloud. Our procedure proves to be effective in reducing the Hausdorff distance presented in the simplified point clouds while the resulting point clouds appear more similar to the original.