It is well known that turbine wakes are responsible for considerable power losses in a wind farm. In the last two decades Wind Farm Layout Optimization (WFLO) has been extendedly used to model the minimization of such wake effects in the attempt to maximize the wind farm performance. In this work we address the optimization of the turbine positioning without any constraint to the wind farm area shape, which is assumed to lead to a bigger performance improvement than through any predefined, fixed area. The optimization is carried out by means of the CEGA Genetic Algorithm[1] over a Gaussian analytical wake model[2]. To attain the optimized area a Multi-Objective WFLO (MOWFLO) is performed. Multi-Objective optimization allows optimizing more than one cost function at a time. Multi-Objective results are expressed in terms of Pareto fronts, which allow not restricting the optimization to any predefined relative cost among variables. In this work two variables, the Power Output (O1) as well as the electricity Cable Length (O2) are simultaneously optimized. After a stagnation condition is attained, single-objective WFLOs can be performed over the optimized areas in order to define the final turbine positioning. The WFLO is carried out in the location of Horns Rev. Horns Rev is a wind farm in operation off the western coast of Denmark, which has been traditionally considered in multiple wind energy scientific works. During the wind farm area shape optimization, the area size is constrained to the size of Horns Rev wind farm. Preliminary results show that the Variable-Area MOWFLO approach is capable to outperform the Power Output of the baseline Horns Rev wind farm layout at the same time that the Cable Length is remarkably shortened, without exceeding the Horns rev area size. Figure 1 shows the obtained Pareto Front after a 40h run (a) and the optimized wind farm layout for the Pareto solution with the highest Power Output (b).