We introduce a partially linear single-index proportional hazards model with current status data. We consider efficient estimations and effective algorithms in the model. We use polynomial splines to estimate both the cumulative baseline hazard function and the nonparametric link function with monotonicity constraint and with no such constraint, respectively. We propose a simultaneous sieve maximum likelihood estimation for regression parameters and nuisance parameters which are approximated by polynomial splines, and show that the resultant estimator of regression parameter vector is asymptotically normal and achieves the semiparametric information bound if the nonparametric link function is truly a spline. We conduct simulation studies to examine the finite sample performance of the proposed estimation method, and present an analysis of renal function recovery data for illustration.