This paper studies best finitely supported approximations of one-dimensional probability measures with respect to the L r -Kantorovich (or transport) distance, where either the locations or the weights of the approximations' atoms are prescribed. Necessary and sufficient optimality conditions are established, and the rate of convergence (as the number of atoms goes to infinity) is discussed. In view of emerging mathematical and statistical applications, special attention is given to the case of best uniform approximations (i.e., all atoms having equal weight). The approach developed in this paper is elementary; it is based on best approximations of (monotone) L r -functions by step functions, and thus different from, yet naturally complementary to, the classical Voronoi partition approach.Keywords. Constrained approximation, best uniform approximation, asymptotically best approximation, Kantorovich distance, balanced function, quantile function.When endowed with the metric d r , the space P r is separable and complete, and d r (µ n , µ) → 0 implies that µ n → µ weakly. Note that P r ⊃ P s and d r ≤ d s whenever r < s. On P s , the metrics d r and d s are not equivalent, as the example of µ n = (1 − n −s )δ 0 + n −s δ n shows, for which d s (µ n , δ 0 ) ≡ 1, and yet lim n→∞ d r (µ n , δ 0 ) = 0 for all r < s, and hence µ n → δ 0 weakly. The reader is referred to [12,32] for details on the mathematical background of the Kantorovich distance, and to [17,32] for a discussion of its usefulness in the study of mass transportation and quantization problems.