Reconciliation is a problem where more than one time series need to be temporally disaggregated, while temporary and cross-sectional conditions should be hold. One of the well-known solutions of the problem is the Multivariate Denton Method, which disaggregates the low frequency time series while using high frequency auxiliary indicators for each of the time series. This method is a convex quadratic optimization problem, where the objective function is equal to the sum of some functions which belong to the time series. The problem can be formulated as a multiobjective optimization problem, where all these functions try to be minimized simultaneously instead of the minimization of the sum of the functions. The aim of this paper is to describe how some results from the multiobjective optimization can be used to develop the Multivariate Denton Method. The optimal solution of the multiobjective optimization problem is called Pareto-optimal solution, where none of the objective functions can be decreased without increasing some of the others. One way to find a Pareto-optimal solution is to start from a feasible solution, and decrease all the objective functions step by step, until reaching a Pareto-optimal solution. In this paper a new method to solve reconciliation problem, using the result we mentioned before, is described. This method is similar to the Multivariate Denton Method, which also finds a Pareto-optimal solution of the same multiobjective optimization problem, but typically the two methods find different solutions.