Radio Frequency Identification (RFID) is a technology used for applications demanding identification or tracking of objects. In a typical application, the RFID system consists of RFID tags and RFID readers at its core, which perform the main task of identifying individual objects. These applications require enough number of readers to be deployed in order to provide a complete coverage of all the tags in the given area. This work presents methods for indoor planning of the RFID readers using Particle Swarm Optimization (PSO). In the implementation of this algorithm, three important factors were taken into accounts, which are coverage, cost and the overlapped regions between neighboring readers. The effect of each one of these factors in the result was explained by the different simulation tests. The PSO planning methods were done by building a Graphical User Interface (GUI) software tool using MATLAB. This GUI has an advantage of simplifying the tags location determination process by a graphical input. Certain program was utilized for the simulation of the RFID readers while the required software for connecting the readers and store the received data in the Microsoft Access database was built by Visual Basic. Two methods for planning were implemented and discussed, and each one has its features. The first one named ( the static planning ) in which the readers have fixed locations while the second one named ( the dynamic planning ) in which the readers have no fixed locations which means that the solution has different random locations for the readers, and PSO used to choose the best solution. These two methods are based on the PSO algorithm but with different approaches. The effect of the different PSO parameters was also studied. The static planning produced a solution in no more than 10 iterations while in the dynamic planning, it was found in about 90 iterations. However, the dynamic planning has a better solution than the static by reducing the overlapped regions which may reach to zero while for the first was a 26.25%, and having a better fitness value of the solution.