Virtual simulation testing is crucial for ensuring automated vehicles safety, which offers low cost and good repeatability. The key is to test in various virtual driving scenarios, but often fails to strike a balance between scenario coverage and test efficiency. To address this issue, we propose a scenario generation method based on a Genetic Algorithm optimized Hamiltonian Monte Carlo sampling approach. Specifically, a Markov chain is constructed converging to the joint probability density distribution function of scenario parameters. By defining a Hamiltonian function with a potential energy term related to the posterior distribution and a kinetic energy term, the sampling process moves efficiently towards high probability regions and achieves faster convergence to the target distribution. Moreover, Jensen-Shannon divergence between generated samples and raw data is proposed to evaluate the scenario coverage, and used as the objective function in Genetic Algorithm to optimize the algorithm parameters. The proposed method is validated by lead vehicle deceleration scenarios generation, where an S-shaped deceleration model is proposed to parameterize the scenario and 22,343 segments extracted from a naturalistic driving dataset are used to calibrate the scenario parameters. Subsequently, the joint probability distribution density function of scenario parameters fitted by a gaussian mixture model is imported into our proposed method. As the results, 1,649 concrete lead vehicle deceleration scenarios are generated with a coverage of 99.14% for the dataset. Compared with the previous Markov Chain Monte Carlo sampling method, our method achieved 9 times higher coverage with 28 times fewer scenarios.