In the design process of industrial construction, is vitally important to know which is the optimum location of different areas of work that make up a manufacturing process and facilities and auxiliar services. The Facilities Layout Problem, so called FLP integrates all industrial activities and has become since the 60's in one of the classic problems of combinatorial optimization, in which many international researchers works. Until the 90's, the approach to the problem was basically a single objective approach, which is clear focused on minimizing the cost of transporting materials or people between the different production and service areas. Using heuristic optimization techniques, which seek to minimize the computation time and facilitate the search for minimal, although local, since the solution space is so large, it is difficult to guarantee the existence of a global minimum. 
However, the criterion of cost is not the only thing to consider in this type of approach, because there are a set of indicators that are vital to ensure that the proposed solution has a proper feasibility level. During the last decade, due in part to increased technological development with the advent of computers and software more developed, have thrived multiobjective approaches to the problem of plant distribution. 
The main objectives of this work are: the creation of a state of the art of the indicators have been used in the literature for the resolution on the ground, getting a set of independent enough that can be used in obtaining distributions optimal plan. We will investigate whether to define a new indicator covering the fundamental objectives of the physical layout provided by different authors. 
Once selected the set of indicators a new multi-objective optimization technique based on simulated annealing algorithm will be proposed. The technique uses a set of solutions that are "indifferent", that are good from the point of view of the different criteria used in the optimization, constituting the so-called Pareto frontier. This makes the optimization technique more complex because we must consider all the parameters that control it as many times as criteria are considered. 
Finally, we present the results of experiments, using the proposed multi-objective optimization technique, for a widely used problem in the literature, the Armour and Buffa problem of 20 activities.  Different Pareto frontiers are obtained introducing complementary points to the historical frontier, exploring the possibility of extending the optimization to 3 indicators.