In recent years several highly effective algorithms have been proposed for Automatic Test Pattern Generation (ATPG). Nevertheless, most of these algorithms too often rely on different types of heuristics to achieve good empirical performance. Moreover, there has not been significant research work on developing algorithms that are robust, in the sense that they can handle most faults with little heuristic guidance. In this paper we describe an algorithm for ATPG that is robust and still very efficient. In contrast with existing algorithms for ATPG, the proposed algorithm reduces heuristic knowledge to a minimum and relies on an optimized search algorithm for effectively pruning the search space. Even though the experimental results are obtained using an ATPG tool built on top of a Propositional Satisfiability (SAT) algorithm, the same concepts can be integrated on application-specific algorithms.