For complex engineering systems, the usual way to estimate their reliability is to run simulations. If the resulting estimate does not satisfy the desired reliability level, we must replace some components with more reliable and again run simulations. This can take several iterations, so the required computation time often becomes unrealistically long. It is known that it is possible to speed up computations if components belong to a few types, and components of each type are identical. So, a natural idea to deal with the general case is to use the general granularity idea, i.e., to group components with similar reliability characteristics into a single cluster, and for each component from the cluster, use the same average reliability instead of the original (somewhat different) reliability characteristics. The accuracy of the resulting approximate estimate of the system’s reliability depends on how exactly we divide the components into clusters. It is therefore desirable to select the clustering that leads to the most accurate reliability estimate. In this paper, we describe an algorithm for such optimal clustering.