Performance failure has become the major threat to the robustness and reliability of various memory and analog circuits. It is challenging to accurately estimate the extremely small failure probability when failed samples are distributed in multiple disjoint failure regions. In this paper, we develop a novel meta-model based importance sampling (MIS) method. MIS utilizes Gaussian Process meta-model to construct quasi-optimal importance sampling distribution, and performs Markov Chain Monte Carlo (MCMC) simulation to generate new samples from the proposed distribution. By updating our global Importance Sampling estimator in an iterated framework, MIS leads to better efficiency and higher accuracy. For SRAM bit cell with single failure region, MIS uses 4-6X fewer samples and reaches better accuracy when compared to several recent methods. For a two-stage amplifier circuit with multiple failure schemes, MIS is 213X faster than MC without compromising accuracy, while other methods fail to cover all failure regions in our experiment.