Software testing is particularly expensive for developers of high-assurance software, such as software that is produced for commercial airborne systems. One reason for this expense is the Federal Aviation Administration's requirement that test suites be modified condition/decision coverage (MC/DC) adequate. Despite its cost, there is evidence that MC/DC is an effective verification technique, and can help to uncover safety faults. As the software is modified and new test cases are added to the test suite, the test suite grows, and the cost of regression testing increases. To address the test-suite size problem, researchers have investigated the use of test-suite reduction algorithms, which identify a reduced test suite that provides the same coverage of the software, according to some criterion, as the original test suite, and test-suite prioritization algorithms, which identify an ordering of the test cases in the test suite according to some criteria or goals. Existing test-suite reduction and prioritization techniques, however, may not be effective in reducing or prioritizing MC/DC-adequate test suites because they do not consider the complexity of the criterion. This paper presents new algorithms for test-suite reduction and prioritization that can be tailored effectively for use with MC/DC. The paper also presents the results of empirical studies of these algorithms.