In many real-life optimization problems involving multiple agents, the rewards are not necessarily known exactly in advance, but rather depend on sources of exogenous uncertainty. For instance, delivery companies might have to coordinate to choose who should serve which foreseen customer, under uncertainty in the locations of the customers. The framework of Distributed Constraint Optimization under Stochastic Uncertainty was proposed to model such problems; in this paper, we generalize this formalism by introducing the concept of evaluation functions that model various optimization criteria. We take the example of three such evaluation functions, expectation, consensus, and robustness, and we adapt and generalize two previous algorithms accordingly. Our experimental results on a class of Vehicle Routing Problems show that incomplete algorithms are not only cheaper than complete ones (in terms of simulated time, Non-Concurrent Constraint Checks, and information exchange), but they are also often able to find the optimal solution. We also show that exchanging more information about the dependencies of their respective cost functions on the sources of uncertainty can help the agents discover higher-quality solutions.