The data rates for indoor users can be boosted using low power consuming nodes like Femtos base stations (FBSs) in long term evolution networks. The manner in which Femtos are deployed inside a building environment, with large number of users, can significantly affect the throughput and number of handovers among Femtos. In our system model, we take into account the following parameters: co-channel interference between FBS and macro BSs, wall attenuation factor and user density in the enterprise building environment. In this work, we formulate two mixed integer linear programming (MILP) optimization models: optimal constant threshold signal to interference plus noise ratio (OptCTSINR) which guarantees a certain minimum SINR and also minimizes the number of Femtos needed for the coverage of enterprise buildings and optimal handover (OptHO) which reduces the number of handovers when the user passes through a certain portion (i.e., within a room or corridor) of the building. We solve these MILP models by utilizing branch and cut framework of CPLEX solver using General Algebraic Modeling System (GAMS) tool. When compared to K-means clustering based placement scheme, for a given number of Femtos, proposed OptCTSINR scheme results in an average SINR improvement of 28 %. Similarly, our proposed OptHO scheme reduces 30 % of the unnecessary handovers when compared to OptCTSINR scheme. © 2016, Springer Science+Business Media New York.