Stochastic Trust-Region Response-Surface method (STRONG) is a new response-surface-based framework for simulation optimization. The appeal of STRONG lies in that it preserves the advantages, yet eliminates the disadvantages, of traditional response surface methodology (RSM) that has been used for more than 50 years. Specifically, STRONG does not require human involvement in the search process and can guarantee to converge to the true optimum with probability one (w.p.1). In this paper, we propose an improved framework, called STRONG-X, that enhances the efficiency and efficacy of STRONG to widen its applicability to more practical problems. For efficiency improvement, STRONG-X includes a newly-developed experimental scheme that consists of construction of optimal simulation designs and an assignment strategy for random number streams to obtain computational gains. For efficacy improvement, a new variant, called STRONG-XG, is developed to achieve convergence under generally-distributed responses, as opposed to STRONG and STRONG-X where convergence is guaranteed only when the response is normal. An extensive numerical study is conducted to evaluate the efficiency and efficacy of STRONG-X and STRONG-XG. Moreover, two illustrative examples are provided to show the viability of STRONG-X and STRONG-XG in practical settings.