Solving constraint optimization problems is hard because it is not enough to find the best solution; an algorithm does not know a candidate is the best solution until it has proven that there are no better solutions The proof can be long, compared to the time spent to find a good solution In the cases where there are resource bounds, the proof of optimality may not be achievable and a tradeoff needs to be made between the solution quality and the cost due to the time delay We propose a decision theoretic meta-reasoning-guided COP solver to address this issue By choosing the action with the estimated maximal expected utility, the meta-reasoner finds a stopping point with a good tradeoff between the solution quality and the time cost.