Genetic circuits promise to revolutionize biofuels, biomanufacturing, and medicine through the use of genetic circuits. However, it is more difficult to verify genetic circuit designs than designs in most traditional engineering disciplines because genetic circuits are inherently stochastic. In particular, genetic circuits are subject to low-probability failure states known as glitches. To more rapidly estimate the probability of these rare glitching behaviors, multiple stochastic simulation algorithms have been developed which make use of a variance reduction technique known as importance sampling. We implemented several such algorithms and found that they are not reliable enough when used on a benchmark suite for use in the computer-aided design of genetic circuits. Using our results, we formulated a mathematical theory of how and why these algorithms fail and formed proposals for how more robust algorithms may be developed.