An efficient semantic query optimization algorithm is proposed, in which all possible transformations are tentatively applied to the query. Instead of physically modifying the query, the transformation process classifies the predicates into imperative, optional or redundant. At the end of the transformation process, all the imperative predicates are retained while the redundant predicates are eliminated. Optional predicates are retrained or discarded based on the estimated cost/benefit of retaining them. The issue of the grouping of semantic constraints to reduce the overhead of retrieving constraints and checking whether each constraint is relevant to the current query is also addressed. Based on the proposed algorithm, a prototype semantic query optimizer has been built and preliminary experiments show that the optimizer performs well for large databases.