Preference logic programming (PLP) is an extension of constraint logic programming for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. PLP essentially separates the programming of a problem itself from the criteria specification of its optimal solutions. The main challenge to implement a PLP system is that how the defined solution preferences take effects automatically on pruning suboptimal solutions and their dependents during the computation. In this paper, we present a tabled resolution, which applies dynamic programming strategies on solving PLP programs. Solution preferences can be properly propagated into recursion through a memoized recursive algorithm, so that a given recursive subgoal only needs to be solved once and always returns the preferred solutions. The strategy has been successfully implemented on a logic programming system. The experimental results show preference logic programming provides a declarative method for optimization problems without sacrificing efficiency.