<p>Given the emerging spider wasp optimization (SWO) algorithm’s advantages in rapid and high-precision solutions to single-objective problems, and to our knowledge, the algorithm currently lacks a multi-objective version; we proffer a multi-objective spider wasp optimization algorithm based on sparse species conservation (SSCMOSWO). Simulating the biological performance of spider wasps, this study enhances the search phase and the mating behavior-related position updates of the spider wasp optimization algorithm to equalize convergence and diversity. Incorporating multiple search mechanisms of spiders and wasps with a non-dominated sorting approach, a dynamic elite external archive (DEEA) is obtained to bootstrap the candidates to converge expediently; a sparse species conservation (SSC) mechanism is devised for adaptive selection of species seeds to ensure population diversity. This study employs 23 benchmark test functions and five foundational engineering problems to compare the SSCMOSWO algorithm with seven well-known multi-objective optimization algorithms, confirming the effectiveness and preliminary practicality of SSCMOSWO in solving multi-objective problems. The analysis of the IGD metric on WFG3–WFG9 and large-scale benchmark suites further demonstrates the potential of SSCMOSWO in addressing many-objective and large-scale optimization problems. Building on these experimental results, this paper applies SSCMOSWO to peak-valley time-of-use price optimization for flexible charging of household electric vehicles, comparing it with four other high-performing evolutionary algorithms. The experimental results further validate the superior performance of SSCMOSWO.</p>