Abrupt environmental changes can lead to evolutionary shifts in both optimal value and variance of descendants in trait evolution. Current methods mainly focus on detecting shifts in optimal values, with less attention to variance. We use a multi-optima and multi-variance OU process model to describe the trait evolution process with shifts in both optimal value and variance. Furthermore, we propose a new method to detect the shifts in both variance and optimal values based on minimizing the loss function with L1 penalty; and implement our method in a new R package, ShiVa. We conduct simulations to compare our method with the two methods considering only shifts in optimal values (l1ou; PhylogeneticEM). Our method shows better predictive ability and includes far fewer false positive shifts in optimal value when shifts in variance exist. We applied our method to the cordylid data. ShiVa outperformed l1ou and phyloEM, exhibiting the highest log-likelihood and lowest BIC.