Maximum likelihood estimation (MLE) is the optimal estimator for target micro-motion feature parameter extracting. However, the grid search will cause the enormous computational amount, and the cost function of laser detection of micro-Doppler echo signals has high nonlinearity and exists many local maxima. A new method combining the mean likelihood estimation and the Monte Carlo method is proposed to solve this problem. A closed-form expression of maximum likelihood parameter estimation is derived. Then the compressed likelihood function is designed to obtain the global maximum. The parameters are estimated by Monte Carlo method sampling and calculating the circular mean value. The dependence of hight accurate initial values and the complex iteration algorithms are avoided in this method, and the joint estimation of parameters can be realized. Furthermore, for multi-component micro-Doppler signal, the presented algorithm can separate the micro-motion component signals at the same time with the estimations, which will not add complexity of algorithm. Applied to the simulated and experimental data, the proposed method achieves similar performance as MLE with less computational complexity. Meanwhile, this method guarantees the global convergence and realizes signals separation and parameters estimation.