Given two sets DA and DB of multidimensional objects, a spatial region R, and a critical distance dc, an optimal-nearestneighbor (ONN) query retrieves outside R, the object in D B with maximum optimality. Let CAR (Sp, p) be the cardinality of the subset Sp of objects in DA which locate within R and are enclosed by the vicinity circle centered at p with radius dc. Then, an object o is said to be better than another one o′ if (i) CAR (So, o) > CAR (S0′ o′), or (ii) when CAR (So, o) = CAR (So′, o′) the sum of the weighted distance from each object in So to o is smaller than the sum of the weighted distance between every object in So′: and o′. This type of queries is quite useful in many decision making applications. In this paper, we formalize the ONN query, develop the optimality metric, and propose several algorithms for finding optimal nearest neighbors efficiently. Our techniques assume that both DA and DB are indexed by R-trees. Extensive experiments demonstrate the efficiency and scalability of our proposed algorithms using both real and synthetic datasets.