The performance of anytime algorithms having a nondeterministic nature can be improved by solving simultaneously several instances of the algorithm-problem pairs. These pairs may include different instances of a problem (like starting from a different initial state), different algorithms (if several alternatives exist), or several instances of the same algorithm (for non-deterministic algorithms). A straightforward parallelization, however, usually results in only a linear speedup, while more effective parallelization schemes require knowledge about the problem space and/or the algorithm itself. In this paper we present a general framework for parallelization, which uses only minimal information on the algorithm (namely, its probabilistic behavior, described by a performance profile), and obtains a super-linear speedup by optimal scheduling of different instances of the algorithm-problem pairs. We show a mathematical model for this framework, present algorithms for optimal scheduling, and demonstrate the behavior of optimal schedules for different kinds of anytime algorithms.