We present a semianalytical method based on the backward master equation to find the optimal transition path time (TPT) of self-propelled run-and-tumble particles (RTPs) under stochastic resetting. The particle moves in a one-dimensional transition region $[x_A,x_B]$ with two absorbing boundaries. It resets at rate $\gamma$ to position $x_R$. Upon each resetting event, the particle either reverses its velocity direction with probability $\eta \in [0,1]$ or retains it with probability $1-\eta$. From the backward master equation we derive exact analytical results for the splitting probability, the mean TPT, and the coefficient of variation ($\mathrm{CV}$) of the TPT distribution. A semianalytical solution for the optimal TPT is also obtained. The mean TPT depends nonmonotonically on $\gamma , \alpha$, and $\eta$, reaching a minimum at optimal parameter values. For large transition regions, resetting the particle to its initial position can accelerate escape compared with reset-free dynamics. In the strong-resetting limit for $\eta >0$ when $x_R=x_A$, the probabilistic reversal upon resetting effectively replaces natural tumbling. The asymptotic TPT then becomes independent of the tumbling rate $\alpha$ and reduces to the ballistic traversal time $L/v$. A speed-reliability trade-off emerges: the parameters that minimize the mean TPT do not generally minimize the $\mathrm{CV}$. Efficient search requires balancing a short mean TPT against high predictability.

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• Escape problems
• Stochastic resetting