A multidatabase system (MDBS) integrates information from multiple autonomous local databases. Performing global query optimization to achieve efficient query processing in such a system is challenging due to local autonomy of the data sources. Dynamic factors in the environment make the problem even more difficult. In this paper, we present two techniques, i.e., contention space partitioning and cost error controlling, to perform global query optimization in a dynamic MDBS. Both techniques generate an execution plan with multiple versions for a query in a dynamic MDBS, utilizing the multistate cost models built for the dynamic environment via our previous multistate query sampling method. The first technique partitions the contention space of a dynamic multidatabase environment into a given number of subspaces and chooses a good query execution plan version for each subspace, while the second technique selects a set of execution plan versions by using a given error tolerance to control query execution costs. Experiments demonstrate that the proposed techniques are quite promising for performing global query optimization in a dynamic MDBS. Compared with related work on dynamic query optimization, our approach has an advantage of avoiding the high overhead for modifying or re-generating an execution plan for a query based on dynamic runtime information.