Multiple Input Multiple Output (MIMO) radar systems has attracted lately a lot of attention thanks to its advantage over the classical phased array radar systems. We site among these advantages the improvement of parametric identifiability, achievement of higher spatial resolution and design of complex beampatterns. In colocated multiple-input multiple-output radar systems, it is usually desirable to steer transmitted power in the region-of-interest in order to increase the Signal to Noise Ratio (SNR) and reduce any undesired signal and thus improve the detection process. This problem is also known as transmit beampattern design. To achieve this goal, conventional methods optimize the waveform covariance matrix, R, for the desired beampattern, which is then used to generate the actual transmitted waveforms. Both steps require constrained optimization. Most of the existing methods use iterative algorithms to solve these problems, therefore their computational complexity is very high which makes them hard to use in practice especially for real time radar applications. In this paper, we provide a closed-form solution to design the covariance matrix for a given beampattern in the three dimensional space using planar arrays, which is then used to derive a novel closed-form algorithm to directly design the finite-alphabet constant-envelope waveforms. The proposed algorithm exploits the two-dimensional discrete Fourier transform which is implemented using fast Fourier transform algorithm. Consequently, the computational complexity of the proposed beampattern solution is very low allowing it to be used for large arrays to change the beampattern in real time. We also show that the number of required snapshots in each waveform depends on the beampattern and that it is less than the total number of transmit antennas. In addition, we show that the proposed waveform design method can be used with non symmetric beampatterns. The performance of our proposed algorithm compares favorably with the existing iterative methods in terms of mean square error.